Please answer Step 1 to Step 4 There are two datasets diamonds test summer 2023 xlsx new diamonds summer 2023 xlsx Step 1 Understanding the Model diamondscontains the data used to build the regression model new diamonds contains the data for the diamonds the company would like to purchase Both datasets contain carat, cut, and clarity data for each diamond Only thediamondsdataset has prices Carat represents the weight of the diamond and is a numerical variable Cut represents the quality of the cut of the diamond, and falls into 5 categories fair, good, very good, ideal, and premium Each of these categories are represented by a number, 1 5, in the Cut Ord variable Clarity represents the internal purity of the diamond, and falls into 8 categories I1, SI2, SI1, VS1, VS2, VVS2, VVS1, and IF Each of these categories are represented by a number, 1 8, in the Clarity Ord variable Using Excel (or other application with which you are familiar), build the linear regression model using the diamonds dataset Based on the Summary Output produced by the regression analysis write out the linear regression model and explain why you are confident (or not confident) in the model to predict the price NOTE Copy and paste the summary output into your report According to the linear model, if a diamond is 1 carat heavier than another with the same cut and clarity, how much more would the retail price of the heavier diamond be Why If you were interested in a 1 5 carat diamond with a Very Good cut (represented by a 3 in the model) and a VS2 clarity rating (represented by a 5 in the model), what retail price would the model predict for the diamond Does color effect the price of the diamond If the color code of the diamond specified in part 3 is H, I, or J, how much cheaper or more expensive will this diamond be compared to any other color of the same diamond Does the regression model estimated in part 4, produce residuals that are normally distributed Explain your answer you may use a graphic to support your explanation Step 2 Calculate the predicted price for each diamond Using thenew diamonds dataset, for each diamond, plug in the values for each of the variables into the linear model (equation), then solve the equation to get the estimated, or predicted diamond price Note For this step, simply use the model developed in part three, which does not include the color code information Step 3 Visualize the Data Create two scatter diagrams (or scatter plot) Plot 1 Plot the data for the diamonds in the database, with carat on the x axis and price on the y axis Plot 2 Plot the data for the diamonds for which you are predicting prices with carat on the x axis and predicted price on the y axis Note If you know how, you can also plot both sets of data on the same chart in different colors What strikes you about this comparison After seeing this plot, do you feel confident in the model's ability to predict prices Why or why not Step 4 The Recommendation Now that you have the predicted price for each diamond, it's time to calculate the bid price for the whole set Note The diamond price that the model predicts represents the final retail price the consumer will pay The company generally purchases diamonds from distributors at 70 of that price, so your recommended bid price should represent that What bid do you recommend for the jewelry company Please explain how you arrived at that number (CLO 1,4,5,6,7)

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Please answer Step 1 to Step 4 There are two datasets. diamonds_test_summer_2023.xlsx new_diamonds_summer_2023.xlsx Step 1 - Understanding the Model: diamondscontains the data used to build

Please answerStep 1 to Step 4

There are two datasets.

diamonds_test_summer_2023.xlsx

new_diamonds_summer_2023.xlsx

Step 1 - Understanding the Model:

diamondscontains the data used to build the regression model.
new diamonds contains the data for the diamonds the company would like to purchase.

Both datasets contain carat, cut, and clarity data for each diamond. Only thediamondsdataset has prices.

Caratrepresents the weight of the diamond and is a numerical variable.
Cutrepresents the quality of the cut of the diamond, and falls into 5 categories: fair, good, very good, ideal, and premium. Each of these categories are represented by a number, 1-5, in theCut_Ordvariable.
Clarityrepresents the internal purity of the diamond, and falls into 8 categories: I1, SI2, SI1, VS1, VS2, VVS2, VVS1, and IF. Each of these categories are represented by a number, 1-8, in theClarity_Ordvariable.

Using Excel (or other application with which you are familiar), build the linear regression model using the diamonds dataset.

Based on the Summary Output produced by the regression analysis write out the linear regression model and explain why you are confident (or not confident) in the model to predict the price. NOTE: Copy and paste the summary output into your report.
According to the linear model, if a diamond is 1 carat heavier than another with the same cut and clarity, how much more would the retail price of the heavier diamond be? Why?
If you were interested in a 1.5 carat diamond with aVery Goodcut (represented by a 3 in the model) and aVS2clarity rating (represented by a 5 in the model), what retail price would the model predict for the diamond?
Does color effect the price of the diamond? If the color code of the diamond specified in part 3 is H, I, or J, how much cheaper or more expensive will this diamond be compared to any other color of the same diamond?
Does the regression model estimated in part 4, produce residuals that are normally distributed? Explain your answer; you may use a graphic to support your explanation.

Step 2 -Calculate the predicted price for each diamond:Using thenew diamondsdataset, for each diamond, plug in the values for each of the variables into the linear model (equation), then solve the equation to get the estimated, or predicted diamond price.Note: For this step, simply use the model developed in part three, which does not include the color code information.

Step 3 -Visualize the Data:Create two scatter diagrams (or scatter plot).

Plot 1 - Plot the data for the diamonds in the database, with carat on the x-axis and price on the y-axis.
Plot 2 - Plot the data for the diamonds for which you are predicting prices with carat on the x-axis and predicted price on the y-axis.
Note: If you know how, you can also plot both sets of data on the same chart in different colors.

What strikes you about this comparison?
After seeing this plot, do you feel confident in the model's ability to predict prices? Why or why not?

Step 4 -The Recommendation:Now that you have the predicted price for each diamond, it's time to calculate the bid price for the whole set. Note: The diamond price that the model predicts represents the final retail price the consumer will pay. The company generally purchases diamonds from distributors at 70% of that price, so your recommended bid price should represent that. What bid do you recommend for the jewelry company? Please explain how you arrived at that number.

(CLO 1,4,5,6,7)