Question 1 ( The height of the father (y) is linearly related to the height of their sons (x). In a sample of fathers and 9-year-old sons, the following data were collected: T = 122 cm, s = 36 cm2, g = 180 cm, s = 25 cm2 %3D You are also told that the covariance between x and y is 15. Which of the following represents the least squares estimate of the regression line between the father's and son's height? Oy= 46.994 + 0.417x Oy = 129.126 + 0.417x Oy = 14 + 0.6x O Cannot be determined because the sample size was not provided. Oy= 106.8 + 0.6x

A weight-loss program wants to test how well their program is working. The company selects a simple random sample of 51 individual that have been using their program for 15 months. For each individual person, the company records the individual's weight when they started the program 15 months ago as an x-value. The subject's current weight is recorded as a y-value. Therefore, a data point such as (205, 190) would be for a specific person and it would indicate that the individual started the program weighing 205 pounds and currently weighs 190 pounds. In other words, they lost 15 pounds. When the company performed a regression analysis, they found a correlation coefficient of r = 0.707. This clearly shows there is strong correlation, which got the company excited. However, when they showed their data to a statistics professor, the professor pointed out that correlation was not the right tool to show that their program was effective. Correlation will NOT show whether or not there is…

Isabelle is a crime scene investigator. She found a footprint at the site of a recent murder and believes the footprint belongs to the culprit. To help identify possible suspects, she is investigating the relationship between a person's height and the length of his or her footprint. She consulted her agency's database and found cases in which detectives had recorded the length of people's footprints, x, and their heights (in centimetres), y. The least squares regression line of this data set is: y = 2.488x + 114.001 omplete the following sentence: The least squares regression line predicts that someone whose footprint is one centimetre longer should be centimetres taller.

A data set is given below. Next question (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x= 3.5000, s, = 2.5100, y = 3.9333, s, = 2.0176, and r=-0.9635, determine the least-squaros regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a) 1 4 4 6 6 6.0 6.4 4.3 3.4 16 19 (a) Choose the correct graph below. O A. C B. OD. There appears to be V relationship Statcrunch Next O A

In a study of 2005 model cars, a researcher computed the least squares regression line of miles per gallon (mpg) on weight (in pounds). They obtained the following equation for this line: mpg = 34.576 - 0.007weight Based on the least-squares line, we would predict a 2005 model car with weight equal to 3,000 pounds would have a mpg of Fill in the blank with: ##.###

An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage. v. The une ory tor ine most recent m odel year. Complete parts (a) through (d) accompanying data represent the weights of various domestic cars and their miles per gallon in BEE Click here to view the weight and gas mileage data (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y = 0.00702 * + (43.2) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice.answer trom part a to find this answer O A. For every pound added to the weight of the car, gas mileage in the city will decrease by mile(s) per gallon, on average. A weightless car will get miles per gallon, on averag B. For every pound added to the weight of the car, gas…

Pre-study and post-study scores for a class of 120 students were considered. The residual plot for the least squares regression line showed no pattern. The least squares regression line was y-hat = 0.2 + 0.9x with a correlation coefficient r=0.85. What percent of the variation of post-study scores can be explained by the variation in pre-study scires? A) 72.3% b) 85.0% c) 90.0% d) 92.2% e) we can not determine with the information given

Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of thecompany wants to know how different variables affect the sales of his vehicles. A random sample ofyearly data was taken with the view to testing the model:SALES=?+?AGE+?MIL+?ENGWhere SALES= amount that a vehicle is sold for($000’s), AGE = age of the vehicle, MIL= the totalmileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data wasprocessed using MINITAB and the following is an extract of the output obtained:The regression equation is *****PredictorCoef StDev t-ratio p-valueConstant1.7586 0.2525 6.9648 0.0000AGE 0.2124 0.3175 * 0.5042MIL -0.75270.3586 -2.0991 **ENG 4.8124 0.6196 7.7664 0.0000Analysis of VarianceSource DF SS MS F pRegression3 413.1291 138.7097*** 0.00Error 50 457.7607 2.2888Total 53a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values ‘*’, ‘**’, ‘***’and ‘***’. d) Hence…

Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of thecompany wants to know how different variables affect the sales of his vehicles. A random sample ofyearly data was taken with the view to testing the model:SALES=?+?AGE+?MIL+?ENGWhere SALES= amount that a vehicle is sold for($000’s), AGE = age of the vehicle, MIL= the totalmileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data wasprocessed using MINITAB and the following is an extract of the output obtained: a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values ‘*’, ‘**’, and ‘***’. d) Hence test whether ? is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis. f) Given an interpretation of the term “R-sq” and comment on its value.

Draw a scatterplot of the right foot temperature (y) versus the left foot temperature (x). Then draw the least-squares regression line on the graph.