Regression Analysis Mini-Case Assignment Scenario

M6 Regression Analysis Mini-Case Assignment Scenario Script: Caption

M6: Regression Analysis Mini-Case Assignment Scenario Script: Caption :
Princess Foods wants to determine if there is a relationship in the amount a household spends on prepared foods to family size and income. Parthika : Well, we still have data collected from a previous marketing study. Let’s use that. We have an Excel file. I am sure we can find the spreadsheet.

It should have the exact information we need. Liwei : Yes, this could be interesting. We may find enough evidence to rethink the meal preparation kits again. Bonnie : Great idea. We need to know it the data is a good fit and what the exact relationship is between the dependent variable and the independent variables.

We can use this information to help us design perhaps a new line of prepared frozen foods. Parthika Yes, what about the prepackaged salad bowls. We really need to see this data. Bonnie :
Yes, let’s get right on this. Mini-Case Assignment Please use the attached spreadsheet and Excel to determine the equation that represents the relationship and explain the goodness of fit.

Based on the data, write a memo and interpret the results. How might this data be used? Dollars spent on Prepared food Family size Gross monthly income 495.......................................

Paper For Above instruction

Princess Foods, a leading company in the food industry, is exploring the relationship between household expenditures on prepared foods and two potential predictor variables: family size and gross monthly income. The objective is to determine whether these independent variables significantly influence the amount of money households spend on prepared foods and to develop a regression equation that accurately models this relationship. Utilizing an existing dataset from a prior marketing study, the company aims to analyze the data using Excel, perform regression analysis, interpret the goodness of fit, and discuss potential applications of the findings to inform strategic decision-making, such as product development and targeted marketing initiatives.

The dataset includes variables such as dollars spent on prepared foods, family size, and gross monthly income, with sample data points exemplified by figures such as a family spending $495 on prepared foods. The analysis begins with inputting the data into Excel, followed by running multiple regression analysis to establish the statistical relationship among the variables. Key outputs include the regression equation, R-squared value, t-statistics, and p-values, which collectively indicate the strength and significance of the model.

The regression equation will be of the form:

Y = β₀ + β₁X₁ + β₂X₂ + ε

where Y is the dependent variable (dollars spent on prepared foods), X₁ is family size, and X₂ is gross monthly income. The coefficients (β₀, β₁, β₂) are estimated through Excel's regression tool and describe the impact of each independent variable on household spending. A positive β coefficient indicates a direct relationship; a negative suggests an inverse relationship.

The goodness of fit is primarily assessed through the R-squared statistic, which indicates the proportion of variability in household spending explained by family size and income. A higher R-squared value signifies a better model fit. Additionally, significance is evaluated through the p-values of each predictor; p-values below 0.05 generally indicate statistically significant relationships.

Interpreting the results, if the model shows that income and family size are significant predictors of spending, Princess Foods can tailor its marketing strategies accordingly. For example, higher income households may spend more on prepared foods, and larger families may also have higher expenditures. The coefficients' magnitudes reveal how much household spending increases with each additional family member or dollar increase in income, guiding product targeting and promotional campaigns.

These insights can inform strategic decisions such as developing new product lines, like prepackaged salad bowls or frozen meal kits, tailored toward demographic groups identified as high spenders. Additionally, the company can allocate marketing efforts efficiently by focusing on demographic segments likely to respond favorably, thereby optimizing advertising budgets and maximizing sales potential.

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