regression & correlation assingment 1

8/4/2019 Regression & Correlation Assingment 1

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Assignment 1Regression

Note:

Submission would be in the form of Hard copy (submit in the class) & Soft copy email [email protected])

Report should mention Group number along with Names & Roll no of each member Total Marks = 20 Answers should be filled within the rectangular boxes. Marks would be deducted for

overwriting.

Question 1

Following is the Data for monthly electrical usage for various home sizes:

Home_Size Monthly_Usage1,710 1,571

1,290 1,1822,930 1,954

2,230 1,840

1,840 1,7112,400 1,956

1,980 1,804

1,470 1,264

1,600 1,4931,350 1,172

Variables:

The dependent variable in this example is the electrical power usage, Y. The independent

variable is the home size, X.-Electrical Power Usage, Y (Kilowatt-hours)

-Home Size, X (Square feet)

1a) Draw a Scatter plot (using SPSS). Interpret the scatter plot and explain the relationship

between the dependent & the independent variable. (4 marks)


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As we see that it is a linear or straight line curve, which shows the absolute relationship between

the two variables. And it also shows that, there is a positive correlation between the two

variables. Here the r= 1 (correlation is equal to one).In this plot there is one outlier also, which is

located at 2900. But such kind of outlier is non influential in nature. Such outliers sometimes

work against the two variables? If we ignore that outlier or change their value or analyze the datawith or without outlier, then the data will be analyzed correctly with no errors.

1b) Run a regression model and interpret the regression results. (3 marks)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .912a

.832 .811 133.438

a. Predictors: (Constant), Home Size, X (Square feet)

Coefficients


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Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 578.928 166.968 3.467 .008

Home Size, X (Squarefeet)

.540 .086 .912 6.288 .000

a. Dependent Variable: Electrical Power Usage, Y (Kilowatt-hours)

In the above regression model the r value is above 0.7 which means that the model can be used

for prediction of the electric usage based on the home size. This also means that home size is the

driving force for high electrical usage.

In the above regression model correlation or the R- value is .912. This is very close to (1) so the

correlation between the two models is greater towards positive correlation.

1c) what strategies can company formulate based on the regression results. Motivate your answer

with explanation. (3 marks)

In the above regression problem the level of significance is .000 which means that there is a

direct influence of home size on the electrical power usage.

The company can focus on homes with big area in square feet to increase their business.

The company can also formulate special offers for people having big homes with high electricalusage to attract more users.

The company can also launch some scheme only for that home with more than 2000 square feet

will get 7 days extra credit than others.


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Question 2

Refer to the Excel Sheet (Big Bazaar.doc) for the chains database and consider the focal

brand Nivea for this question

Data description:

For the supermarket brand "Big Bazaar" 124 weekly data points of one outlet are available

(running from week 46 in 2003 until week 12 in 2006). For each brand / formula combination we

have data on:

Sales (stock-keeping units);

Prices (Rs per l00 ml) (as observed);

Regular price (Rs per 100 ml) (proxy for the price as if there were no discounts);

Variable for feature-only: weighted distribution figure at the chain level for a feature-only(no display) in a certain week. A feature-only means special out-of-store attention for the

brand, either in the store flier or in an ad in a newspaper or a magazine.

Variable for display-only: weighted distribution figure for display-only (no feature) in a certain

week. A display-only means special in-store attention for a brand: a temporary shelf or box

with products in one of the aisles, or attention around the brand's regular shelf.

Question 1

Develop a regression model (Additive or Multiplicative) to predict the Sales of the focal brand

Nivea and describe each parameter / term of the model. Motivate why you used this type of the

model. (3 marks)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .870a .756 .748 829.1785666

a. Predictors: (Constant), NIVEA FEATURE, NIVEA

DISPLAY, NIVEA Price (Rs), NIVEA Price (Rs)


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Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 6147.683 819.284 7.504 .000NIVEA Price

(Rs)

-7.277 6.189 -.102 -1.176 .242

NIVEA Price

(Rs)

-14.598 4.183 -.274 -3.490 .001

NIVEA

DISPLAY

7868.324 972.397 .493 8.092 .000

NIVEA

FEATURE

10902.201 1393.046 .403 7.826 .000

a. Dependent Variable: NIVEA Sales (Rs)

As it is evident from the above model that R-square value is more than 0.7 which means that

display and feature both has the significant effect on the price of Nivea.

In this model both the dependent variables are the driving force of price.

I used this particular model to see which the most influential variable between the two is.

If the display plays the important role in pricing and sale of the product then we should focus on

display of the product. (or)

If the feature plays the important role in pricing and sale of the product then we should focus on

feature of the product.

Question 2

Interpret the regression output. (4 marks)

As it is evident from the above model that R-square value is more than 0.7 which means that

display and feature both has the significant effect on the price of Nivea.

In this model both the dependent variables are the driving force of price.

In the model level of significance is also .000 which means that there is a direct influence of

display and feature on pricing.


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Question 3

For the following value of coefficients, calculate the Sales for Nivea (3 marks)

(Nivea Regular price = Rs 192.11, Nivea Display = 0.0568548, Nivea Feature = 0.02669355)

Sales= 0.102 * 192.11 + 0. .493 * 0.0568548 + 0.403 * 0.02669355

=19.63

(Group 7) members name and roll no.:-

ClassP.G. Marketing

Mohd. Irfan Rizvi Roll No.52

Reena Gupta Roll No.77

Shruti Dubey Roll No.99

Suroje ghosh Roll No.104

Saad Syed Roll No.83

regression & correlation assingment 1

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