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- the increase in value of a home due to the addition of central air conditioning is best estimated by using The full cost of the purchase and installation of the air-conditioning ½ the cost of the purchase and installation of the air-conditioning The cost of the purchase but not the installation of the air-conditioning The difference in the sales prices of comparable homes with and without air-conditioningMore real estate Consider the Albuquerque home sales from Exercise 29 again. The regression analysis gives the model Price = 47.82 + 0.061 Size. a) Explain what the slope of the line says about housing prices and house size. b) What price would you predict for a 3000-square-foot house in this market? c) A real estate agent shows a potential buyer a 1200-square- foot home, saying that the asking price is $6000 less than what one would expect to pay for a house of this size. What is the asking price, and what is the $6000 called?What is the average miles per gallon (mpg) for all new cars? Using consumer reports, a random sample of 35 new cars gave an average of 21.1 mpg. a) identify the variable b) is the variable quantitative or qualitative c) what is the implied? population?
- What is the F- test statistic and p-valueConsider again the example introduced in Section 4.5 of a credit card company that has a database of information provided by its customers when the customers apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3). Figure 4.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? d. Recall the multiple regression in Figure 4.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts a, b, and c? What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? e. Add the coefficients of determination for the simple linear regression in parts a, b, and c, and compare the result to the coefficient of determination for the multiple regression model in Figure 4.23. What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? f. Add age, a dummy variable for gender, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 4.23. Code the dummy variable for gender as 1 if the customers gender is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?The Paymore Rental Car Agency rents cars in a small town. It wants to determine how many rental cars it should maintain. Based on market projections and historical data, the manager has determined probability distributions for the number of rentals per day and rental duration (in days only) as shown in the following tables: Number of customers per day Probability 0 .20 1 .20 2 .50 3 .10 Rental duration in days Probability 1 .10 2 .30 3 .40 4 .10 5 .10 Design a simulation experiment for the car agency and simulate using a fleet of four rental cars for 10 days. Compute the probability that the agency will not have a car available upon demand. Should the agency expand its fleet? Explain how a simulation experiment could be designed to determine the optimal fleet size for the Paymore Agency. Use the following random numbers in order (from left to right) for the simulation of number of customers per day: 0.62 0.48 0.96 0.86 0.86 0.29 0.79 0.22 0.08 0.62…
- You must use Excel to perform the regression analysis. Provide the answers under the space provided for each question. You must provide the Excel output for the question along with the answers. Round off the values on the output to three-five decimal places if appropriate.Questions for Real Estate Case Study-Model Building:As preliminary analysis the dataset includes information on 50 homes currently for sale, but some homes have unusually high prices, square footage, and lot sizes. To refine the dataset for analysis, apply the following exclusion criteria:- Exclude any home with a price greater than $1,000,000.- Exclude any home with square footage (SqFt) greater than 3000 ft².- Exclude any home with a lot size greater than 10,000 ft².After performing these exclusions, how many homes remain in the dataset?1) Show the observations that excluded (2p)2) How many categorical variables are present in the dataset? How will you incorporate these categorical variables into the regression…You have the following hypothesis: People who eat at Wendy's also tend to have larger household size (number of household members). To test this hypothesis, you use: OA. Causal research ○ B. Exploratory Research O C. Descriptive Research OD. Non experimental researchIf the probabilities are, respectively 0.09, 0.15, 0.21, and 0.23 that a person purchasinganew automobile will choose the color green, white, red or blue. What is the probability that a given buyer will purchase a new automobile: (a) that is green or red? (b) that is not white?
- Compare your answers for the Cost of Goods Sold for the three valuation methods: weighted mean, FIFO, and LIFO. Which method will produce the greatest profit for Sports Haven?Assume that a person's income directly affects their purchasing power for a particular product. We have a customer named John who has a per capita income of $35,102 that is looking to buy a new car. If the current US population is 335,707,897, the current per capita income is $40,363, and total new car sales in 2022 were $1,131,009,556,950; what would John's per capita spend be for a new car, assuming his income directly affects the amount he can spend?A researcher wants to estimate the average monthly income of individuals in a certain city. A random sample of 20 individuals is selected, and their monthly incomes (in dollars) are recorded as follows: 2,500 2,800 3,000 3,200 3,400 3,500 3,600 4,100 4,200 4,300 4,500 4,600 4,700 4,900 3,700 3,800 4,000 5,000 5,200 5,500 Assuming the incomes are normally distributed, calculate the 90% confidence interval for the average monthly income of individuals in the city.. Please show all the necessary steps and provide your answer with the appropriate interpretation. Hints: • Calculate the sample mean and sample standard deviation. . Determine the critical value (t-value) for a 90% confidence level. • Calculate the margin of error using the critical value and sample standard deviation. • Calculate the lower and upper bounds of the confidence interval using the sample mean and margin of error. • Interpret the confidence interval in the context of the problem. I
