Recall from Chapter 2 that the file "Supermarket Transactions.xlsx" contains over 14,000 transactions made by supermarket customers over a period of approximately two years. To understand which customers purchase which products, use pivot tables to create crosstabs and an associated column part for each of the following. For each, express the counts as percentages so that for any value of the first variable listed, the percentages add to 100%. Do any patterns stand out?

a. State or Province versus Product Department
b. Marital Status versus Product Category
c. Annual Income versus Product Category

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The file "HW_02.xlsx" includes data on 138 houses that were recently sold in a (fictional) suburban community. The data set includes the selling price of each house, along with its appraised value, square footage, number of bedrooms, and number of bathrooms.

a. Create a table of correlations between all of the variables. Comment on the magnitudes of the correlations. Specifically, which of the last three variables—Square Feet, Bedrooms, and Bathrooms—are highly correlated with the Selling Price?

b. Create a scatterplot between Appraised Value and Selling Price.

c. Here, Selling Price minus Appraised Value is the "error" in the appraised value, meaning how much more or less the house sold for than the appraiser expected. Find the correlation between this difference and the Selling Price. Is the correlation reasonably large? What does it mean?

To understand which customers purchase which products, create a table of correlations between all of type variables and comment on the magnitude of the correlations using correlating variables with selling price.

In this question, you are asked to create a table of correlations between all of the variables and comment on the magnitudes of the correlations. The variables to consider are Square Feet, Bedrooms, Bathrooms, and Selling Price. Correlation measures the strength and direction of the relationship between two variables. In this case, you want to find out which of the last three variables (Square Feet, Bedrooms, and Bathrooms) are highly correlated with Selling Price. To do this, you would calculate the correlation coefficients between each pair of variables and examine their magnitudes. The variables with higher correlation coefficients indicate a stronger relationship with Selling Price.

You are instructed to create a scatterplot between Appraised Value and Selling Price. A scatterplot is a visual representation of the relationship between two variables. In this case, you will plot the Selling Price on the y-axis and the Appraised Value on the x-axis. Each data point represents a house, and its position on the plot shows the corresponding values of Selling Price and Appraised Value. By examining the scatterplot, you can identify any patterns or trends in the relationship between these two variables.

Here, you need to find the correlation between the difference (Selling Price minus Appraised Value) and Selling Price. The difference represents the "error" in the appraised value, indicating how much more or less the house sold for than the appraiser expected. By calculating the correlation coefficient, you can determine if there is a reasonably larger correlation and what it means. A higher correlation coefficient suggests a stronger relationship between the two variables, and in this case, it would indicate how much the selling price deviates from the appraised value.

Question:How to create a table of correlations between all of type variables and comment on the magnitude of the correlation?

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ankitkumarrrrrcode ankitkumarrrrrcode · Answer 2 · 2024-03-25T13:51:13-04:00

To analyze the dataset of recently sold houses, you can use correlation analysis to determine the relationships between variables such as Selling Price, Square Feet, Bedrooms, and Bathrooms. Additionally, creating a scatterplot between Appraised Value and Selling Price will visualize their relationship. Calculating the correlation between the difference in appraised value and Selling Price assesses the accuracy of the appraisals.

To create a table of correlations between the variables in the HW_02.xlsx file, you can use Excel's built-in correlation function. By calculating the correlation coefficient, you can determine the strength and direction of the relationship between variables. In this case, you are interested in the correlations between Selling Price, Square Feet, Bedrooms, and Bathrooms. The correlation coefficient ranges from -1 to 1, where values closer to -1 or 1 indicate a stronger relationship.

To create a scatterplot between Appraised Value and Selling Price, you can use Excel's chart feature. Plotting the two variables on a graph will help visualize the relationship between them. This will allow you to see if there is a pattern or trend in how Appraised Value and Selling Price are related.

To find the correlation between the difference (error) in the appraised value and Selling Price, you can calculate the correlation coefficient using Excel's correlation function. A larger correlation coefficient indicates a stronger relationship. In this context, a larger correlation between the difference and Selling Price would suggest that the appraised value is less accurate, leading to a larger difference between the expected and actual selling price.

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