Can you solve this using Excel?

Question 2: The data set in tab Seattle Home Values contains information about the assessed value and characteristics of houses in the Seattle zip code of 98105. Value: Assessed value in US dollars Bedrooms: Number of bedrooms in the house Bathrooms: Number of bathrooms in the house Sqft Living: Square footage of living space indoor Lotsize: Size of lot in square feet For parts a-e, conduct a simple regression using the variables Value and Sqft Living a) Make a scatterplot of the variables Value and Sqft Living. Include a trendline. b) Calculate the correlation coefficient and interpret your result. c) Calculate the RP of the simple regression model and interpret your result. d) Work out a simple linear regression model to predict the value from the square footage of living indoor space. Is the square footage a significant predictor of assessed value at the 5% significance level? Report a statistic and p-value. c) What is the predicted assessed value of houses with 2,500 square feet of living space? Compute a 95% confidence interval for this mean. For parts f-h, conduct a multiple regression using all the variables in the data set. f) Calculate the adjusted R of the multiple regression model and interpret your result. g) Work out a multiple linear regression model to predict the value from the four variables. Which variables are significant predicators of assessed value at the 5% significant level? Report a statistic and p-value for each variable. h) What is the predicted assessed value of houses with 2,500 square feet of living space, 4 bedrooms, 3 bathrooms, and a lot size of 4,200 square feet? Question 2: The data set in tab Seattle Home Values contains information about the assessed value and characteristics of houses in the Seattle zip code of 98105. Value: Assessed value in US dollars Bedrooms: Number of bedrooms in the house Bathrooms: Number of bathrooms in the house Sqft Living: Square footage of living space indoor Lotsize: Size of lot in square feet For parts a-e, conduct a simple regression using the variables Value and Sqft Living a) Make a scatterplot of the variables Value and Sqft Living. Include a trendline. b) Calculate the correlation coefficient and interpret your result. c) Calculate the RP of the simple regression model and interpret your result. d) Work out a simple linear regression model to predict the value from the square footage of living indoor space. Is the square footage a significant predictor of assessed value at the 5% significance level? Report a statistic and p-value. c) What is the predicted assessed value of houses with 2,500 square feet of living space? Compute a 95% confidence interval for this mean. For parts f-h, conduct a multiple regression using all the variables in the data set. f) Calculate the adjusted R of the multiple regression model and interpret your result. g) Work out a multiple linear regression model to predict the value from the four variables. Which variables are significant predicators of assessed value at the 5% significant level? Report a statistic and p-value for each variable. h) What is the predicted assessed value of houses with 2,500 square feet of living space, 4 bedrooms, 3 bathrooms, and a lot size of 4,200 square feet