A sports dataset collected over ten years shows that the total number of baskets scored by an NBA player in a season depends on the amount of his yearly salary. Thesample mean and standard deviation of salaries of studied players over this period are $1.1 million dolars and $0.4 million dollars, respectively. The sample mean andstandard deviation of total number of scored baskets in a season are 701.7 and 57.1, respectively.The correlation coefficient between the yearly salary and the number of baskets is 0.75.Based on this data, fit a linear regression model and predict the total number of baskets that will be scored in a season by an NBA player with salary $3.9 million dollarsa year. Write your answer as an integer, by rounding your solution to the nearest integer.Number of baskets in a season:

Let Y be the number of baskets in a season and X be the amount of salary. Given that mean = 1.1 and…

Based on this data, fit a linear regression model and predict the total number of baskets that will be scored in a season by an NBA player with salary $3.9 million dollars a year. Write your answer as an integer, by rounding your solution to the nearest integer.

Based on this data, fit a linear regression model and predict the total number of baskets that will be scored in a season by an NBA player with salary $3.9 million dollars a year. Write your answer as an integer, by rounding your solution to the nearest integer.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Gordon borrowed $420 from Lyn and agreed to pay her back $24 every month. Write a formula (linear model) showing the amount of money Gordon still owes at any time. Clearly define your variables.
A biology student is investigating the claim that the temperature can be predicted by counting cricket chirps. She has found a linear regression equation T=42.2+0.21r, where T is the temperature in degrees Fahrenheit and r is the number of chirps per minute. What is the predicted temperature for 90 chirps per minute? 18.9 degrees 227.6 degrees 23.3 degrees 61.1 degrees
A real estate agent has developed a linear model for the price of a house, P, in dollars in terms of the area, A, in square feet for the homes in a certain neighborhood. The data set had areas ranging from 1,000 square feet to 4,500 square feet. Would predicting the prices of a home that is 4,900 square feet be interpolation or extrapolation? Explain. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is outside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is outside the range of the areas in the data.
Please see below.
Hello can you please break down these questions for me. I don't know what to do after I calculate the z-score The mean of the distribution is 100 and the SD is 15 What is the probability that a randomly selected student from this population would have an IQ score of 115 or greater? Calculate the z-score Z= (115-100)/15= 1 What is the probability that students have IQ score of 130 or below? What is the probability that students have IQ score of between 85 and 115?
The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. 220- 210 200 190 180 170 160 150 Year 0 130- 120- 110- 100- 90 801 6 12 15 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. Retail Sales 85.851 108.426 141.781 169.256 202.297 222.266 3 6 9 12 15 Use the above scatter plot to decide whether the linear model fits the data well. The function is a good model for the data. The function is not a good model for the data Estimate the retails sales in the U. S. in 2015. billions of dollars.
A. Develop an estimated regression equation that can be used to predict the price of the stock based on the number of shares of company's stocks sold and the volume of exchange. B. Interpret the coefficients of the estimated regression equation that you found in Part a. C. If in a given day, the number of shares of the company that were sold was 94,500 and the volume of exchange on the New York Stock Exchange was 16 million, what would you expect the price of the stock to be?
The percentage of mothers who smoke cigarettes during pregnancy has declined approximately linearly from13.1% in 1997 to 12.3% in 2004 Predict the percentage in 2015.
The marching band at a local high school is selling candy bars. The profits will go toward the purchase of new marching band uniforms. They are interested in knowing what price they should charge to obtain the maximum profit. The candy bars are donated by a local retailer so they know that if they charge $0 they will make $0 profit. They also collect data from other marching bands who have sold candy bars in the past. The data they collected is shown in the table below. Price 0.00 0.45 0.60 0.75 1.00 1.25 1.40 1.50 Profit 0.00 $720 $900 $990 $845 $625 $560 $300 What factors might influence how much is to be made from the candy sale? Give at least two. What happens to the profit as the price of each candy bar increases? Explain possible reasons for your observations. How do you suppose the other bands determined the price at which to sell each candy bar? Do any data values seem unusual? Yes or…