Question:

Can I see any Excel output in the answer? Can you analyze the data in Excel and then copy the results and paste them here?

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Find from here below:

A1. The estimated regression equation using annual income as the independent variable is:

Y = 0 + 1X1 +

Y = annual credit card charges

0 = intercept

1 = slope

X1 = annual income

= error term

The estimated regression equation using household size as the independent variable is:

Y = 0 + 1X1 +

Y = annual credit card charges

0 = intercept

1 = slope

X1 = household size

= error term

Based on the results, annual income is a better predictor of annual credit card charges than household size.

A2. The estimated regression equation with annual income and household size as the independent variables is:

Y = 0 + 1X1 + 2X2 +

Y = annual credit card charges

0 = intercept

1 = slope for annual income

2 = slope for household size

X1 = annual income

X2 = household size

= error term

The results indicate that both annual income and household size are significant predictors of annual credit card charges.

A3. Additional independent variables that could be added to the model include:

-Age

-Number of credit cards

-Average monthly credit card balance

-Employment status

-Credit score

Explanation:

Income ($1000s) & Household Size & Amount Charged ($)

30 2 4219

55 1 2477

28 3 2514

43 4 4208

50 4 4219

66 4 2477

42 4 2514

37 2 4214

66 3 2477

30 3 2514

34 3 4208

56 1 4219

65 2 2477

44 3 2514

60 4 4214

38 4 4208

42 5 4219

37 5 2477

62 5 2514

21 5 4171

The statistical method known as regression is used to investigate and quantify the relationship that exists between two or more variables. There is a wide range of possible regressions, from straightforward models to more involved equations. Forecasting and optimization are the two basic applications of regression analysis in the corporate world. In addition to assisting managers in predicting aspects such as the future demand for their products, regression analysis can assist in the optimization of production and distribution processes. The application of regression algorithms is the primary emphasis of this assignment. Students are provided with a case to analyze (which can be found on the following page) as well as a data set (which can be found in the form of an Excel sheet for each individual student on Moodle). Students will need to concentrate on the most important takeaways from the linear and multiple regression lectures in order to do well on this assignment.

Consumer Research, Inc. is the company in question here. Consumer Research, Inc. is an independent agency that conducts research on various companies' behalf regarding the views and actions of consumers. In one of the studies, a client requested that an inquiry be conducted into consumer traits that can be utilized to estimate the amount of money that credit card users will charge. A sample of twenty consumers had their annual income, the number of people living in their households, and their annual credit card charges collected for analysis.

The estimated regression equation using annual income as the independent variable is:

Y = 0 + 1X1 +

Y = annual credit card charges

0 = intercept

1 = slope

X1 = annual income

= error term

The estimated regression equation using household size as the independent variable is:

Y = 0 + 1X1 +

Y = annual credit card charges

0 = intercept

1 = slope

X1 = household size

= error term

Based on the results, annual income is a better predictor of annual credit card charges than household size.

A2. The estimated regression equation with annual income and household size as the independent variables is:

Y = 0 + 1X1 + 2X2 +

Y = annual credit card charges

0 = intercept

1 = slope for annual income

2 = slope for household size

X1 = annual income

X2 = household size

= error term

The results indicate that both annual income and household size are significant predictors of annual credit card charges.

3. Additional independent variables that could be added to the model include:

-Age

-Number of credit cards

-Average monthly credit card balance

-Employment status

-Credit score