SRM UNIVERSITY

SCHOOL OF MANAGEMENT SOFTWARE SOLUTIONS FOR BUSINESS

( MBN 605 )

LAB MANUAL II-MBA

III-SEMESTER

ACADEMIC YEAR 2011-2012

LESSON PLAN MMBBNN660055 SSOOFFTTWWAARREE SSOOLLUUTTIIOONNSS FFOORR BBUUSSIINNEESSSS LL TT PP CC

-- -- 22 11

Exercise. No

Topic Duration

1 SPSS - Introduction 2 periods

2 Entering Data in SPSS 2 periods

3 Mean, Median & Mode 2 periods

4 Database Design 2 periods

5 Enter data for Questionnaire 2 periods

6 Descriptive Analysis 2 periods

7 Chi-Square Analysis 2 periods

8 Anova Analysis 2 periods

9 Correlation Analysis 2 periods

10 T-Test 2 periods

11 Company Creation 2 periods

12 Ledger Creation 2 periods

13 Profit & Loss A/c, Balance Sheet 2 periods

14 Creation of Stock group, Stock Category, Godown, Unit of Measure

2 periods

15 Creation of Stock item

2 periods

REFERENCE BOOKS 1. Carver, Doing Data Analysis with SPSS !0.0, Thomson Learning,2001 2.Namrata Agrawal,Financial Accounting using Tally 6.3, Dreamtech Press, New Delhi,2002 3.David Whigham, Business Data Analysis Using Excel, Oxford University Press, first Indian Edition 2007.

EXERCISE: 1

SPSS (Statistical Package for Social Sciences)

Introduction

SPSS for Windows is a user-friendly statistical package that enables comprehensive statistical analysis and charting. The SPSS interface consists of windows, menus and dialogue boxes but a traditional command language is also available for more advanced users. There are several different windows, for example, a Data Editor window in worksheet format, an Output window that displays results and a Chart Editor window for high-resolution graphs.

PASW (formerly SPSS) is a computer program used for statistical analysis. Before 2009 it was called SPSS, but in 2009 it was re-branded as PASW (Predictive Analytics SoftWare).[1] The company announced July 28, 2009 that it was being acquired by IBM for US$1.2 billion.

Developer(s) SPSS Inc.

Initial release 1968

Stable release 18.0 (Win / Mac / Linux) / 2009

Operating system Windows, Linux / UNIX & Mac

Platform Java

Type Statistical analysis

License Proprietary software

Website www.spss.com

Starting SPSS

To start SPSS, choose SPSS from the Start – Program -- SPSS.

On start up, the Data Editor window is visible:

The Title bar contains the minimise, maximise and close buttons on the right hand side.

The Menu bar consists of the usual Windows menus, plus some specific to SPSS, such as Data, Transform, Analyze and Graph. Details of the menus are given below.

The Menus

SPSS has ten main menus available when you are viewing the Data Editor window. The list of menu options changes when you are in different windows. The menus follow the standard Windows format of having the File and Edit menus to the left-hand side of the screen, and the Window and Help menus on the right. The menus found in the Data Editor window are described in more detail below:

File - The File menu deals with all the file-handling aspects of SPSS. such as opening an existing file, opening a new file, saving a file or printing a file. There is an Apply Data Dictionary command that stores information about your data. This is especially useful if you have received data from elsewhere and you need to know what it is and how it is organised. The command to exit from SPSS when you have finished is in this menu.

Edit - This menu contains commands for undoing the last action, cut, copy and paste actions, and the command for find.

View - The View menu changes some display attributes, such as the appearance of grid lines and whether value labels are displayed instead of codes. The chosen commands are marked with a tick.

Data - The Data menu is used to make global changes to SPSS data files, such as merging files, transposing variables and cases, or creating subsets of cases for analysis. You can insert new variables and new cases, or you can sort your cases according to a particular variable, transpose the cases or weight the cases. These changes are temporary unless you specifically save the file with the changes.

Transform - The Transform menu is used to make changes to selected variables in the data file and to compute new variables based on the values of existing ones. Random numbers can be generated and ranking or recoding of data can also be done. Changes are only temporary unless you specifically save the data file

Analyze - The Analyze menu contains all the categories of statistics that SPSS is capable of performing. The arrows to the right of all the menu options indicate that there is a submenu of more specific tests or groups of tests.

Graphs - The Graph menu enables you to plot graphs from the data. Some of these graphs can also be plotted as part of a wider analysis. SPSS supports the five main chart types - Bar (or Column), Line, Area, Pie and High-Low. It can also produce real histograms, quality control graphs, scatter plots in up to 3 dimensions, and several other specialist graph types (version 9.0 can also produce an ROC curve). One extra graph SPSS supports is the Boxplot, used mainly in non-parametric analysis, or when you want to get a feel for the shape of a small quantity of data.

Utilities - The Utilities menu contains a list of variables currently available from the data sheet; it allows you to define sets of variables that need to be analysed together. Finally the Utilities menu allows you to run a script or change the menus using the menu editor.

Window - The Window menu allows you to select and resize the windows used in SPSS. Most of the commands here can also be done with the mouse.

Help - Contains the commands for the SPSS Help system.

The Main Toolbar

File Buttons

The first three buttons give the three most common commands from the File menu – Open an Existing File, Save the Current File and Print the Current File respectively.

Dialog Recall

This button gives you immediate access to the last 12 dialog boxes you were working with. This is extremely useful if you are building up an analysis and constantly going back to the same box to change or modify an option.

Go to Chart

This button allows you to go to an open Chart Editor.

Go to Data

When in a window other than the Data Editor window this button will return you to the Data Editor.

Go to Case

This button allows you to go directly to a specific case in the data editor. It is useful if you need to edit your data, or if something unusual has cropped up in your analysis and you want to check out the source data.

Variables

Clicking on this button produces a dialog box containing a list of all the variables defined in the data file. Selecting a variable from this list displays the variables properties - its name, label, type, information about missing values and the value labels. This box can be kept open whilst you work with the data file so that you can examine a variable's information as you examine the results of an analysis.

Find

This button allows you to perform a simple search to find a value.

Insert Case / Insert Variable

Data entry, being the tedious exercise that it is, is fraught with errors. Consequently it is not unusual to find yourself wanting to add a case or a variable in the middle of ones already created. These two buttons will add a blank row or column in your data set to allow you to do that.

Split File/Weight Cases/Select Cases

These buttons allow you to do three of the Data menu commands with ease.

Value Labels

In SPSS you can create meaningful labels for non-meaningful numerical data. This button allows you to display the labels in the data editor so that you don’t have to remember what the numbers meant.

Use Sets

In SPSS you can group variables together into sets so that the variables can be analysed together. This button allows you to specify what sets of the ones you have defined you want to use.

Designate Window Button

SPSS allows you to have more than one Output or Syntax window open at any one time so that the results you create can be sorted into different files. One of the Output windows has to be the Designated Window. This is indicated by an exclamation mark (!) in the title bar. If you want an Output or Syntax window to become the designated window, make it the active window and click this button on the toolbar.

Note: The Designated Window is not the same as the Active Window. The Active Window is the window at the forefront of your screen. Anything you type, or any menus you use should happen in the Active window. Of all the windows you have open on your desktop, only one will be the Active Window. The term Designated Window only applies to Output and Syntax windows which act as a destination for SPSS produced text. There is one designated Output window at any one point in time plus one designated Syntax window.

Run Commands

This button allows you to run commands from the Syntax window. SPSS will begin from the current insertion point and go through the commands until it finishes.

The Toolbar below the menus provides quick access to some of the more commonly used menu options. Pointing to a button on the Toolbar will display its function. The buttons visible on the Toolbar will depend on which SPSS window is active at the time.

The Data Editor

This window is where you enter, edit, view, transform and label the data that you want to analyse. Each column represents a single variable (for example, age or height) and each row represents a single observation or case (a respondent in a survey, for example).

Although the Data Editor has rows and columns, it is not a spreadsheet like Microsoft Excel. Cells only contain values that you type or generate with commands. They cannot contain formulas that update values from data based in other cells.

Unlike other types of SPSS windows, it is only possible to have one Data Editor open at any time.

Defining Variables

Before entering data, you should define it by naming each variable and specifying its type (for example, numeric, string or date) and its length or display width.

To define variables select the Variable View tab located at the bottom of the Data Editor. In this view, each variable is now represented by a row and each column represents a characteristic of the variable such as name, type or length:

Variable Names

To assign (or change) a variable name click in the appropriate cell in the Name column and type in the new name. So if your first variable is to be called Age, type 'Age' into the Name column of the first row. Note that SPSS has the following restrictions for variable names:

• Must not exceed 8 characters. • Must not include spaces or special characters (e.g., - or *) though underscores are

acceptable. • Must not start with numbers.

Variable Type

To change the variable type:

• Click in the appropriate cell in the Type column.

• Click the small grey box that appears in the cell to display the Variable Type box:

• Specify the type by clicking the circle to the left of the appropriate type. • Click OK after specifying the type.

String is for any variable that contains 'alpha' characters, (anything other than numbers) as data. Numeric is the most common and is the default type.

For numeric variables, you can set the width (number of digits) and the number of decimal places (number of digits to the right of the decimal point) in this dialogue box. These may also be set using the Width and Decimals columns on the Variable View sheet where clicking on a cell produces either a grey box or up/down arrows to make selections.

Variable Labels

Variable labels allow you to attach longer descriptions to variable names. These labels may be displayed in SPSS output to make the output more meaningful.

To specify a variable label, type it into the appropriate Labels column on the Variable View sheet. Variable labels may be up to 256 characters.

Value Labels

An example of the use of value labels might be where your data represents the value 'Present' by the number 1 and 'Absent' by the number 2 or 'Male' by the letter M and 'Female' by the letter F.

To specify value labels:

• Click in the appropriate cell in the Values column and click the grey box to view the Value Labels dialogue box:

• Type the appropriate value and associated label, up to 60 characters, then click

Add. • When you are finished, click OK.

Missing Values

Some data contain numbers that distinguish between different types of missing values. For example, on a survey the number -999 might represent respondents who refused to answer a question, while -998 might indicate those for whom the question did not apply.

To define or change missing values:

• Select the appropriate cell in the Missing column and click the grey box to show the Missing Values dialogue box:

• Check the circle to the left of the best description of your missing values. The default is No Missing Values.

Discrete allows specification of 1 to 3 specific missing values.

Range indicates all the values between two points such as -99 to 0 should be treated as missing. A combination of a range and one discrete value can be specified.

• When you are finished, click OK.

Column Format

The Width and Align columns on the Variable View sheet allow specification of variable column width and text alignment.

To adjust the column width:

• Click on the appropriate cell, then use the up and down arrows to change the number of characters allowed for the variable.

The column width may also be adjusted by clicking and dragging the column borders between variable names on the Data View sheet.

Align:

• The alignment property indicates whether the information in the Data View should be left-justified, right-justified, or centered

Measurement Scales

You may specify the level of variable measurement as Scale, Ordinal or Nominal by selecting the appropriate cell in the Measure column. This setting does not affect most SPSS analyses, but does affect some graphing procedures.

In SPSS you can specify the level of measurement as scale (numeric data on an interval or ratio scale), ordinal, or nominal. Nominal and ordinal data can be either string alphanumeric) or numeric.

Nominal. A variable can be treated as nominal when its values represent categories with no intrinsic ranking; for example, the department of the company in which an employee works. Examples of nominal variables include region, zip code, or religious affiliation.

Ordinal. A variable can be treated as ordinal when its values represent categories with some intrinsic ranking; for example, levels of service satisfaction from highly dissatisfied to highly satisfied. Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.

For ordinal string variables, the alphabetic order of string values is assumed to reflect the true order of the categories. For example, for a string variable with the values of low, medium, high, the order of the categories is interpreted as high, low, medium which is not the correct order. In general, it is more reliable to use numeric codes to represent ordinal data.

Scale. A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.

Exercise: 2

Entering Data

When you have finished defining your data, select the Data View tab in the Data Editor Window then:

• Select a cell on the Data View sheet and type the data value you wish to enter. • Press return. The value will appear in the cell and the cell below will be selected

for entering the next data value.

Pressing the tab key instead of return will enter the data and move the cursor to the next column.

Note: SPSS will not allow you to type alpha characters into columns defined as numeric. To use characters specify the Type as 'string' on the Variable View sheet.

Data From Files

If your data is already stored in a file, SPSS has a Text Import Wizard to assist with reading in raw (ASCII text) data files.

• Under the File menu choose Read Text Data. • In the Open file dialogue box, select the file you wish to read. The Text Wizard

will then take you step by step through reading your data.

Note that your data file may contain variable names in the first row.

Saving Data

To save the data and data definitions you have entered:

• Select Save from the File menu or click the save icon in the button bar. When first saving a file, you must specify a file name and location.

• Select the drive where you wish to save your data. • In the 'Save as' type field, choose SPSS (*.sav) to save the file as an SPSS for

Windows data set. or

• Choose SPSS Portable (*.por) as the type if you plan to move your data to an operating system that cannot read SPSS Windows native data sets.

• Click Save.

Analysing Data

The statistical functions of SPSS are accessed via the Analyze menu. Often, the first thing you will want to do is to obtain some descriptive statistics. To do this:

• From the Analyze menu select Descriptive Statistics/Descriptives:

• Choose the variables you wish to analyse from the list on the left of the Descriptives dialogue box, click on each one and then click the arrow button. The selected variable(s) will move to the Variable(s) list on the right

.

You can move variables back and forth in this way until you have the list you want to analyse.

You can move more than one variable at a time by selecting one and holding the control key (CTRL) down as you select others.

Select contiguous variables by clicking and dragging the mouse down the variable list, or by holding the shift key and selecting the first and last variable in a series.

• When you have selected your list of variables, click OK.

Results of the analyses will appear in an SPSS Output window which by default will automatically move in front of the Data Editor:

To view all the output, use the scroll bars to the right and along the bottom of the window, if necessary. You can move to a specific area of the output by selecting the appropriate item in the outline on the left.

Large segments of output are only partially displayed in the output navigator. A red arrow pointing down indicates that some output is not displayed. (Note: A red arrow pointing to the right simply indicates that an object is selected.)

To view an entire section of output, double-click the area that you wish to view. You will then be able to scroll through the entire section of output.

Creating Graphs And Charts

To create a graph or chart of your data:

• With the Data Editor active, choose a graph type, for example Scatter, from the Graphs menu.

• Select a type of scatter plot required then click the Define button. • From the Variables dialogue box, select the variables you want plotted and assign

them to an axis, using the arrows:

• At this stage you can also add a title to your graph and alter other plotting options. • Click the OK button.

The graph is plotted and placed below any previous output in the Output window.

Printing Output

To obtain hard copy of the output, choose Print from the File menu or select the printer icon on the toolbar. To print a single table or chart:

• Select the object you would like to print in the output window or outline. A box will appear around the selected output.

• Choose print.

When printing only a selected part of an output file, check the Selection option in the Print range dialogue box:

Editing Output

You can also edit, cut and paste tables and charts in the Output window.

To edit elements within an output table, double-click the table so that a hashed line surrounds it. Elements of the tables may now be edited by selecting them with the mouse.

Double-clicking a chart will open it in the SPSS Chart Editor. When you have finished editing the chart, close the Chart Editor to return to your output.

To cut or copy part of your output, highlight the object either in output window or in the outline on the left. Then click Edit on the top menu and select Cut or Copy.

Use Copy Objects to copy tables or graphs as images and retain their original formatting. The object(s) may be pasted elsewhere in the SPSS output or into another program such as MS Word.

The Hide and Show buttons in the Toolbar allow you to hide and show parts of your output temporarily for printing purposes. Select the object you wish to hide or show from the contents outline on the left. Then click the appropriate button. (Some parts of output such as notes are hidden by default.)

You may permanently delete parts of the SPSS output by selecting them and choosing Edit then Delete from the SPSS menu.

The Chart Editor

To edit a chart, double-click on it to open a Chart Editor window. The menus and Toolbar buttons will change. The main menus, File, Edit, View, Analyze, Graphs and Help remain but now there are new menus of Gallery, Chart, Series and Format:

The Gallery menu allows you to choose the type of chart to display. The menu is a cut-down version of the Graph menu.

The Chart menu allows you to add and alter the peripheral objects of a chart, such as the title, legend and other sorts of annotation.

The Series menu lets you alter the way the series is displayed, including the omission of certain values, and lets you transpose data directly on the graph.

The Format menu alters just about everything else on the graph itself – the colours, the font types and sizes, the style of graph and the perspective. It also allows you to add an interpolation or regression line through a set of points.

The Chart Editor Toolbar contains the base tools already described plus tools specific to working with charts.

Saving Output

To save output from data analysis or graph plotting to a file, click the save icon in the Toolbar, or choose Save from the File menu.

The file type for SPSS output is Output files (*.spo). These files may only be opened in SPSS. As an alternative you may choose Export from the File menu and save your output

in ASCII text (*.txt) which can be read by any text editor or word processor or HTML (*.htm) format for use on the World Wide Web.

Note: When text (*.txt) format is selected, charts are visible only as separate JPEG (*.jpg) image files and are not converted to a text format.

Opening Existing Files

To open existing SPSS files:

• Choose Open file from the File menu or use the Open File icon on the toolbar. • Choose the type of file to open from the 'Files of Type' box. This may be .sav,

.spo, .sys or .sbs. • Use the 'Look in box' section to specify the file location. • Double click the file to open.

Note: Excel spreadsheets may be read and saved directly through the File menu.

Some files that are not accessible through the Open File window (e.g., MS Access) are accessible through ODBC. Select Open Database/New Query from the File menu.

Syntax Windows

If, after preparing an analysis dialogue box, or a graph box, you click the Paste button instead of the OK button, an SPSS command performing exactly the same action is pasted into a designated Syntax window. If no Syntax window is open a new one is created. The command is not run but is left in the Syntax window for you to edit and run later. In this way you can build up your own command files by creating them via the menus and dialogue boxes.

The Help System

SPSS has an extremely thorough Help system accessible from the Help menu. The Topics section allows you to choose from a contents list or an index, or run a search for a topic.

The Tutorial section is good for a general overview for the first-time user. It covers the basics of using SPSS for Windows and introduce you to the major concepts involved. It is strongly recommended that if you are new to SPSS, you go through the Tutorial before you attempt to do anything else with the program. Select Tutorial from the Help menu.

The Statistics Coach is designed to help you find the best way of achieving the analysis you want. Context-sensitive help is also always available either from the Toolbar or from a Help button in dialogue boxes.

Descriptive Statistics Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Descriptive Statistics are used to present quantitative descriptions in a manageable form. In a research study we may have lots of measures. Or we may measure a large number of people on any measure. Descriptive statistics help us to simply large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary.

Univariate Analysis

Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that we tend to look at:

• the distribution • the central tendency • the dispersion

In most situations, we would describe all three of these characteristics for each of the variables in our study.

The Distribution. The distribution is a summary of the frequency of individual values or ranges of values for a variable. The simplest distribution would list every value of a variable and the number of persons who had each value. For instance, a typical way to describe the distribution of college students is by year in college, listing the number or percent of students at each of the four years.

Table 1. Frequency distribution table.

One of the most common ways to describe a single variable is with a frequency distribution. Depending on the particular variable, all of the data values may be represented, or you may group the values into categories first (e.g., with age, price, or temperature variables, it would usually not be sensible to determine the frequencies for

each value. Rather, the value are grouped into ranges and the frequencies determined.). Frequency distributions can be depicted in two ways, as a table or as a graph. Table 1 shows an age frequency distribution with five categories of age ranges defined. The same frequency distribution can be depicted in a graph as shown in Figure . This type of graph is often referred to as a histogram or bar chart.

Table . Frequency distribution bar chart.

Distributions may also be displayed using percentages. For example, you could use percentages to describe the:

• percentage of people in different income levels • percentage of people in different age ranges • percentage of people in different ranges of standardized test scores

Central Tendency. The central tendency of a distribution is an estimate of the "center" of a distribution of values. There are three major types of estimates of central tendency:

• Mean • Median • Mode

Mean

The Mean or average is probably the most commonly used method of describing central tendency. To compute the mean all you do is add up all the values and divide by the number of values. For example, the mean or average quiz score is determined by summing all the scores and dividing by the number of students taking the exam. For example, consider the test score values:

15, 20, 21, 20, 36, 15, 25, 15

The sum of these 8 values is 167, so the mean is 167/8 = 20.875.

Median

The Median is the score found at the exact middle of the set of values. One way to compute the median is to list all scores in numerical order, and then locate the score in the center of the sample. For example, if there are 500 scores in the list, score #250 would be the median. If we order the 8 scores shown above, we would get:

15,15,15,20,20,21,25,36

There are 8 scores and score #4 and #5 represent the halfway point. Since both of these scores are 20, the median is 20. If the two middle scores had different values, you would have to interpolate to determine the median.

Mode

The mode is the most frequently occurring value in the set of scores. To determine the mode, you might again order the scores as shown above, and then count each one. The most frequently occurring value is the mode. In our example, the value 15 occurs three times and is the model. In some distributions there is more than one modal value. For instance, in a bimodal distribution there are two values that occur most frequently.

Notice that for the same set of 8 scores we got three different values -- 20.875, 20, and 15 -- for the mean, median and mode respectively. If the distribution is truly normal (i.e., bell-shaped), the mean, median and mode are all equal to each other.

Dispersion

Dispersion refers to the spread of the values around the central tendency. There are two common measures of dispersion, the range and the standard deviation. The range is simply the highest value minus the lowest value. In our example distribution, the high value is 36 and the low is 15, so the range is 36 - 15 = 21.

The Standard Deviation is a more accurate and detailed estimate of dispersion because an outlier can greatly exaggerate the range (as was true in this example where the single outlier value of 36 stands apart from the rest of the values. The Standard Deviation shows the relation that set of scores has to the mean of the sample. Again lets take the set of scores:

15,20,21,20,36,15,25,15

to compute the standard deviation,

Correlation

The correlation is one of the most common and most useful statistics. A correlation is a single number that describes the degree of relationship between two variables

Inferential statistics Inferential statistics are used to draw inferences about a population from a sample. Consider an experiment in which 10 subjects who performed a task after 24 hours of sleep deprivation scored 12 points lower than 10 subjects who performed after a normal night's sleep. Is the difference real or could it be due to chance? How much larger could the real difference be than the 12 points found in the sample? These are the types of questions answered by inferential statistics. There are two main methods used in inferential statistics: estimation and hypothesis testing. In estimation, the sample is used to estimate a parameter and a confidence interval about the estimate is constructed. In the most common use of hypothesis testing, a "straw man" null hypothesis is put forward and it is determined whether the data are strong enough to reject it.

Exercise 3

To find out Mean, Median, Mode, SD & frequency distribution for the data given below and also draw the relevant graph.

S.No Name of the student Gender UG Degree Percentage 1 Ravi Male BBA 56 2 Ram Male B.Com 78 3 Priya Female B.Com 69 4 Shakthi Female BBA 78 5 Aarthi Female B.Sc 89 6 Muthu Male B.Sc 86 7 Uma Female B.Com 67 8 Kalyan Male B.Com 82 9 Kavitha Female BBA 72 10 Kalai Female BBA 65

Procedure: Step 1: Click Start – Programs – SPSS Step 2 : Type all the variable in the variable view Name, Gender, UG Degree, Percentage Step 3: Select the type of variable, width, value, measure Name – String – Nominal Gender – Numeric – Values 1 male, 2 female – Nominal UG Degree – Numeric - -- Values – 1 BBA, 2 B.Com, 3 B.Sc Percentage – Numeric – Scale Step 4: Enter the data in the data view in the corresponding variable column Step 5 : Click Analyze – Descriptive Statistics – Frequencies Step 6: Select Gender, click ok Step 7: Output for frequency will appear Step 8: Click Analyze – Descriptive Statistics – Frequencies Step 9: Click Statistics in frequencies dialog box – select Mean, Median, Mode, & SD Step 10: click continue – click ok Step 11: Output will appear & save the output Step 12: Save the data file & close the SPSS window

Exercise 4

DATA BASE DESIGN FOR THE GIVEN QUESTIONNAIRE

A STUDY ON CUSTOMER SATISFACTION TOWARDS UTENSILS IN BIG

BAZAAR AT BANGALORE

1. Name: 2. Your Age: Please tick the relevant box 1) Less than 20 years 2). 21-30 years 3) 31-40 years 4) Above 40 3. Marital status: 1)Married 2) Unmarried 4. Gender: 1)Male 2)Female 5. Your family annual income: 1) less than Rs. 24,000 2) 25,000 - 1 lakh 3). 1 lakh – 3 lakh 4) Above 3 lakhs 6. Educational Qualification: 1) HSC 2) Under Graduation 3)Most Graduation 4)Others 7. Occupation: 1) Pvt Employee 2)Govt Employee 3)Business 4)House wife 8. Family Type: 1) Joint 2)Nuclear 9. How did you know about Big bazaar? 1)Recommended by friends 2)Through Advertisement 3)Reduction Offer 10. How often do you shop? 1)Once in a week 2)Fortnightly 3)Once in a month 11. Do advertisement and promotion influence your shopping decision? 1) Yes 2) No 12. Which form of advertisement do you think is most effective? 1) Television Radio 2) News Paper 3) Banners 4)Wall Painting 5)Window Display

13. For how long you have become a consumer to Big Bazaar? 1) < 2 yrs 2) 2 to 5 yrs 3) 5 to 10 yrs 4) >10 yrs 14. What was the reason for your loyalty? 1) Price 2)Quality 3) Service Please tick the appropriate box to indicate your degree of satisfaction 15. Quality of the Utensils Q:No Dimensions Excellent Very

Good Good Average To be

improved 15a Quality of utensils in

Big Bazaar

15b Design of the Utensils 15c Copper bottom Utensils 15d Utensils with Heat

proof handle

15e Life of the products 16 Variety of the Utensils Q:No Questions Strongly

agree Agree Neither

Agree or Disagree

Disagree Strongly disagree

16a Variety of Kitchen utilities available in big bazaar

16b Variety of storage & containers utilities available in big bazaar

16c Variety of casseroles available in big bazaar

16d Variety of serve ware available in big bazaar

16e Variety of Pressure Cookers available in big bazaar

17 Service Q:No Dimensions Excellent Very

Good Good Average To be

improved 17a Credit Card service 17b Package of Utensils 17c Exchange of Utensils if

needed

17d Parking area 18 Brand Q:No Dimensions Excellent Very

Good Good Average To be

improved 18a Brand image of Big

Bazaar

18b Brand awareness of Big Bazaar

18c Brand loyalty of Big Bazaar

19 Responsiveness Q:No Dimensions Excellent Very

Good Good Average To be

improved 19a Employees give us

special attention

19b Employees are polite and patient

19c My requests are handled promptly

19d Employees give me good information according to my needs

19e Employees’ knowledge speed up my selection

19f The Employees have enough expert information in Utensils at Big bazaar

20 Price Q:No Questions Excellent Very

Good Good Average To be

improved 20a Costs of the Utensils in

Big bazaar

20b Comparing with Competitors

20c Discount offered by big bazaar

20d Value Pricing in big bazaar

20e Overall satisfaction of price

Exercise 5

ENTER THE DATA FOR THE QUESTIONNAIRE GIVEN IN THE EXERCISE 4 FOR ALL THE 60 SAMPLES

Enter all the collected data of sample size 60 in the data view using SPSS

Exercise 6

DESCRIPTIVE ANALYSIS

Calculate Mean, Median , Frequency distribution, Cross tab for the data set (Exercise 5) for all the variables and also draw relevant graph for each variable.

Exercise 7

HYPOTHESIS formulation AND CHI-SQUARE ANALYSIS

1) Using Chi-Square test , analyze the gender ( Qno: 4) and the various form of advertisement( Qno:12) using the data set (Ex:5) and write the interpretation for the test

Hypothesis Formulation:

Null Hypothesis: There is no relationship between age groups and the form of advertisement

Alternative Hypothesis: There is no relationship between age groups and the form of advertisement

Procedure: Step 1: Click Start – Programs – SPSS Step 2 : Click Analyze – Descriptive Statistics – cross tab – click statistics – select chisquare Step 3: Select age groups and form of advertisement and click ok Step 4: Output will appear & save the output Step 5: Save the data file & close the SPSS window

In the output screen, check the Sig value

Note:

** at the end of the Sig value denotes 1% significant level

If the Sig is less than .01, then null hypothesis is reject at 1% significant level

If the Sig is greater than .01, then null hypothesis is accept at 1% significant level

* at the end of the Sig value denotes 5% significant level

If the Sig is less than .05, then null hypothesis is reject at 5% significant level

If the Sig is greater than .05, then null hypothesis is accept at 5% significant level

2) Using Chi-Square test , analyze the age groups( Qno: 2) and the various form of advertisement( Qno: 12) using the data set (Ex:5) and write the interpretation for the test

3) Using Chi-Square test , analyze the gender( Qno: 4) and the shopping decision ( Qno: 11) using the data set (Ex:5) and write the interpretation for the test

4) Using Chi-Square test , analyze the educational qualification( Qno: 4) and loyalty of the customer towards Big Bazaar ( Qno: 14) using the data set (Ex:5) and write the interpretation for the test

5) Using Chi-Square test , analyze the occupation( Qno: 7) and the no: of years of buying the product in Big Bazaar( Qno: 13) using the data set (Ex:5) and write the interpretation for the test

Exercise: 8

HYPOTHESIS FORMULATION AND ANOVA ANALYSIS

1) Using ANOVA , analyze the no: of years of buying the product ( Qno: 13) and degree of satisfaction level of the quality of the utensil ( Qno: 15) using the data set (Ex:5) and write the interpretation for the test

Hypothesis Formulation:

Null Hypothesis: There is no significance difference between no: of years of buying the product with regard to the degree of satisfaction level of the quality of the utensil advertisement

Alternative Hypothesis: There is significance difference between no: of years of buying the product with regard to the degree of satisfaction level of the quality of the utensil advertisement

Procedure:

Click Analyze—compared means – One – way ANOVA

2) Using ANOVA , analyze the no: of years of buying the product (Qno: 13) and degree of satisfaction level of the Variety of the Utensils (Qno: 16) using the data set (Ex:5) and write the interpretation for the test 3) Using ANOVA , analyze the no: of years of buying the product (Qno: 13)and degree of satisfaction level of the Service of the Utensils (Qno: 17) using the data set (Ex:5) and write the interpretation for the test

4) Using ANOVA , analyze the no: of years of buying the product(Qno: 13) and degree of satisfaction level of the Brand of the utensil(Qno: 18) using the data set (Ex:5) and write the interpretation for the test

5) Using ANOVA , analyze the no: of years of buying the product(Qno: 13) and degree of satisfaction level of the Price of the utensil(Qno: 20) using the data set (Ex:5) and write the interpretation for the test

6) Using ANOVA, analyze the form of advertisement (Qno: 12) and degree of satisfaction level of the quality of the utensil(Qno: 15) using the data set (Ex:5) and write the interpretation for the test

7) Using ANOVA , analyze the form of advertisement(Qno: 12) and degree of satisfaction level of the variety of the utensil(Qno: 16) using the data set (Ex:5) and write the interpretation for the test

8) Using ANOVA , analyze the form of advertisement(Qno: 12) and degree of satisfaction level of the service of the utensil(Qno: 17) using the data set (Ex:5) and write the interpretation for the test

9) Using ANOVA , analyze the form of advertisement(Qno: 12) and degree of satisfaction level of the Brand of the utensil(Qno: 18) using the data set (Ex:5) and write the interpretation for the test

10) Using ANOVA , analyze form of advertisement(Qno: 12) and degree of satisfaction level of the Price of the utensil(Qno: 20) using the data set (Ex:5) and write the interpretation for the test

Exercise 9

HYPOTHESIS FORMULATION AND CORRELATION ANALYSIS

1) Using correlation analysis , analyze the income level (Qno: 5) and degree of satisfaction level of the quality of the utensil using the data set (Ex:5) and write the interpretation for the test

Hypothesis Formulation:

Null Hypothesis:

There is no correlation between income level and the degree of satisfaction level of the quality of the utensil

Alternative Hypothesis:

There is no correlation between income level and the degree of satisfaction level of the quality of the utensil

Procedure:

Click Analyze – correlate --- bivariete

2) Using correlation analysis , analyze the income level (Qno: 5) and degree of satisfaction level of the variety of the utensil using the data set (Ex:5) and write the interpretation for the test

2) Using correlation analysis , analyze the income level (Qno: 5) and degree of satisfaction level of the services of the utensil using the data set (Ex:5) and write the interpretation for the test

2) Using correlation analysis , analyze the income level (Qno: 5) and degree of satisfaction level of the brand of the utensil using the data set (Ex:5) and write the interpretation for the test

2) Using correlation analysis , analyze the income level (Qno: 5) and degree of satisfaction level of the price of the utensil using the data set (Ex:5) and write the interpretation for the test

Exercise 10

HYPOTHESIS FORMULATION AND T-TEST

1) Using T-test, analyze the gender (Qno: 4) and degree of satisfaction level of the quality of the utensil using the data set (Ex:5) and write the interpretation for the test

Hypothesis Formulation:

Null Hypothesis:

There is no significance difference between male and female with respect to the degree of satisfaction level of the quality of the utensil

Alternative Hypothesis:

There is significance difference between male and female with respect to the degree of satisfaction level of the quality of the utensil

2) Using T-test, analyze the gender and degree of satisfaction level of the variety of the utensil using the data set (Ex:5) and write the interpretation for the test

3) Using T-test , analyze the gender and degree of satisfaction level of the Price of the utensil using the data set (Ex:5) and write the interpretation for the test

4) Using T-test , analyze the gender and degree of satisfaction level of the Brand of the utensil using the data set (Ex:5) and write the interpretation for the test

5) Using T-test , analyze the gender and degree of satisfaction level of the Services of the utensil using the data set (Ex:5) and write the interpretation for the test

TALLY

Ex No: 11 Company Creation Aim: To create company using Tally software Procedure: Step 1: Start – Programs—Tally Software Step 2: Click Create Company Step 3: Type the relevant data for the following field Name Mailing Name Address State PIN Code Telephone Email Address Currency Maintain Financial Year Book Beginning from Tally Vault Password Use Security Control Use Tally Audit Features ...etc Step 4: After entered all the details, press enter to accept Step 5: Now, Company is created

EX No: 12 Ledger Creation Aim: To create the ledger in Tally Software Procedure: Step 1: Start – Programs—Tally Software Step 2: Click Select Company Step 3: Gateway of Tally screen will open. Click Account info --- Ledger --- Create Step 4: Enter the name of the ledger in the name field Step 5: Select the corresponding group for the ledger in the “Under” field Step 6: Press “enter” to save the ledger Step 7: Press “ESC” after creation of the entire ledger Step 8: Click Display for viewing all ledger Step 9: Press “ESC” to close the tally window

EX NO: 13 Prepare a Profit and Loss Account and Balance Sheet Aim: To prepare a Profit and Loss Account and Balance Sheet Procedure: Step 1: Start – Programs—Tally Software Step 2: Click Select Company Step 3: Gateway of Tally screen will open. Click Account info --- Ledger --- Create Step 4: Enter the name of the ledger in the name field Step 5: Select the corresponding group for the ledger in the “Under” field Step 6: Press “enter” to save the ledger Step 7: Press “ESC” after creation of the entire ledger Step 8: Click Accounting Voucher for entering transaction Step 9: Enter the transaction in corresponding Voucher ( i.e., payment voucher, sales Voucher, ..etc) Step 10: After finished the entire voucher, press ESC to go back to the gateway Step 11: Click Balance sheet, profit and loss A/c report to display the output Step 12: Press “ESC” to close the tally window

1.3.1995 Contributed capital Rs 90,000 Paid for furniture Rs 3,500 Paid into bank Rs 34,00 2.3.1995 Bought goods Rs 15,000 Bought goods from Gwalior Mills Rs 10,000 3.3.1995 Bought from Premier Mills, Bombay Rs 5,000 4.3.1995 Sold to M/s. Vellore Silk House Rs 15,000 5.3.1995 Sold to Mr. Mariapian Rs. 7,000 7.3.1995 Paid for advertisement Rs 1,000 9.3.1995 Bought Stationery Rs 300 10.3.1995 Paid for printing Rs 200

12.3.1995 Bough textiles from Rs 5,000 Binny Mills,

Bangalore Paid for freight a carriage Rs 200 14.3.1995 Paid Gwalior Mills by cheue Rs 9,800 Discount received Rs 200 16.3.1995 Paid Premier Mills by cheque in full settlement of their account Rs 4,900 18.3.1995 Cash sales Rs 2000 20.3.1995 Received from Mr. Mariappan in full settlement of his account Rs 6,850 21.3.1995 Withdrew cash for personal

Use Rs 1,000 25.3.1995 Paid commission Rs 500 27.3.1995 Paid Rent by cheque Rs 700 31.3.1995 Paid salaries to Manager By cheque Rs 3,000 Paid Salaries Rs 12,000 Solution Date

Particulars Debit Credit Voucher Type

Groups

1.3.1995 Cash A/c Dr To Capital A/c (Being capital contributed)

90000.00 90000.00

Receipt Voucher

Cash Capital

1.3.1995

Furniture A/c Dr To Cash A/c (Being Cash paid for furniture)

3500.00 3500.00

Payment Voucher

Fixed Assets Cash

1.3.1995 2.3.1995 2.3.1995

Bank A/c Dr To Cash A/c (Being Cash Deposited into Bank) Purchase A/c Dr To Cash A/c (Being goods purchase for cash) Purchase A/c Dr

34000.00

34000.00 15000.00

Contra Voucher Purchase Voucher

Bank Cash Purchase Cash

3.3.1995 4.3.1995

5.3.1995

7.3.1995

To Gwalior Mills A/c (Being goods purchased on credit from Gwalior Mills Purchase A/c Dr TO Premier Mills A/c (Being goods purchased on credit from Premier Mills0 Vellore Silk House A/c Dr To Sales A/C (Being goods sold on credit to Vellore Silk House) Mariappan A/c Dr To Sales A/c (Being goods sold on credit to Mariappan) Advertisement A/c Dr To Cash A/c (Being cash paid

10000.00 50000.00 15000.00 15000.00 7000.00 1000.00

10000.00 50000.00

7000.00

Purchase Voucher Purchase Voucher Sales Voucher

Sales Voucher Payment Voucher

purchase Sundry Creditors Purchase Sundry creditors Sundry Dedtors Sasles Sales

Sundry Debtor Sales Voucher Indirect Expenses Cash

9.3.1995 10.3.1995 12.3.1995

12.3.1995 14.3.1995

for advertisement) Stationery A/c Dr To Cash A/c (Being cash paid for stationery) Printing A/c Dr To cash A/c (Being cash paid for printing) Purchase A/c Dr To Binny Mills A/c (Being goods purchased on credit from Binny Mills) Freigh & Carriage A/c Dr To Cash (Being cash paid for Freight & Carriage) Gwalior Mills A/c Dr To Cash A/c

300.00 200.00 5000.00 200.00 10000.00

1000.00 300.00 200.00 5000.00

200.00

Payment Voucher Payment Voucher Puchase Voucher

Payment Voucher Payment Voucher

Indirect Expenses Cash Indirect Expenses Cash Purchase Sundry Creditors

Indirect Expenses Cash Sundry Creditor Cash

16.3.1995 18.3.1995 20.3.1995

21.3.1995 25.3.1995

To Discount Received A/c (Being cash paid to Gwalior Mills) Premier Mills A/c Dr To cash A/c To Discount Received A/c (Being cash paid to Premier Mills) Cash A/c Dr To Sales A/c (Being Goods sold for cash) Cash A/c Dr Discount Allowed A/c Dr To Mariappan (Being cash RECEIVED from Mariappan) Drawings A/c Dr To Cash A/c (Being Cash Withdrawn for personal Use) Commission A/C

5000.00 2000.00 6850.00 150.00 1000.00

9800.00 200.00 4900.00 100.00 2000.00

7000.00 1000.00

Payment Voucher Sales Voucher Receipt Voucher

Payment Voucher

Indirect Income Sundry Creditor Cash Indirect Income Cash Sales Cash Indirect Expenses Sundry Debtor Capital Cash

27.3.1995 31.3.1995 31.3.1995

Dr To Bank A/c (Being cash paid for commission) Rent A/c Dr To Bank (Being Rent paid By cheque) Salary A/c Dr To Bank A./c (Being Salary paid By cheque to Manager) Salary A/c Dr To Cash A/c (Being Salary paid By cash )

500.00 700.00 3000.00 12000.00

500.00 700.00 3000.00 12000.00

Payment Voucher Payment Voucher Payment Voucher Payment Voucher

Indirect Expenses Cash Indirect Expenses Bank Indirect Expenses Bank Indirect Expenses Cash

EX No: 14 Creation of Stock group, Stock Category, Godown, Unit of Measure Aim: To create Stock group, Stock Category, Godown, Unit of Measure in Tally Software Procedure: Step 1: Start – Programs—Tally Software Step 2: Click Select Company Step 3: Gateway of Tally screen will open. Click inventory info --- stock groups --- Create Step 4: Enter the name of the stock group in the name field Step 5: Select the corresponding group for the ledger in the “Under” field Step 6: Press “enter” to save the stock group Step 7: Press “ESC” after creation of the entire stock group Step 8: Click Display for viewing all stock group Step 9: Click stock category – enter the name and respected stock group Step 10: Click godown – enter the name Step 11: Click stock category – enter the name and respected stock group Step 12: Click Unit of Measure– enter the unit and symbol Step 13: Press “ESC” to close the tally window

EX No: 15 Creation of Stock item Aim: To create Stock item in Tally Software Procedure: Step 1: Start – Programs—Tally Software Step 2: Click Select Company Step 3: Gateway of Tally screen will open. Click inventory info --- stock item --- Create Step 4: Enter the name of the stock item in the name field Step 5: Select the corresponding Stock group, Stock Category, Godown &Unit of Measure Step 6: Enter the quantity, rate Step 7: Press “enter” to save the stock item Step 8: Press “ESC” after creation of the entire stock item Step 9: Click Display for viewing all stock item Step 10: Press “ESC” to close the tally window