The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. For a random sample of 33 Cadets, each bought "new" two years ago and each sold "used" within the past month, the least-squares regression equation relating the two variables mileage (denoted by xr) and used selling price (denoted by y, in dollars) was information, test for a significant linear relationship between the two variables by doing a hypothesis test regarding the population slope B, (Assume that the 39.52-0.56x. The standard error of the slope of this least-squares regression line was approximately 0.43. Based on this variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the 0.10 level of significance, and perform a two-tailed test. Then fill in the table below. 图 (If necessary, consult a list of formulas.) The null hypothesis: H : 0 The alternative hypothesis: H :0 OSO The type of test statistic: (Choose one) - The value of the test statistic: (Round to at least three decimal places.) The p-yalue: (Round to at least three decimal places.) 口口 ? 日 B.

The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 0.5 1 1.5 2 3 3.5 4.5 Midterm Grades 63 66 68 72 74 93 94 Table   Step 5 of 6 :  Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆ.

The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Answer How to enter your answer (opens in new window) Bone Density 40 61 62 68 69 357 350 343 340 315 Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. Tables Copy Data Keypad Keyboard Shortcuts Table Previous step answers Submit Answer Dec 3 4:51 VI

Researchers measured the percent body fat and the preferred amount of salt (percent weight/volume) for several children. Here are data for seven children salt:   Preferred amount of salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 20 30 22 30 38   23 30                     Using your calculator or software, what is the equation of the least-squares regression line for predicting percent body fat from preferred amount of salt?  (a) yˆ = 24.2 + 6.0 x (b) yˆ = 0.1 5 + 0.0 1x (c) yˆ = 6.0 + 24.2 x

The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) through (c) below. Click the icon to view the data table. ..... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females. Find the least-squares regression line for males. y =x+O %D/ (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) Find the least-squares regression line for females. y = ý =x+O %3D (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) (b) Interpret the slope of the least-squares regression line for each gender, if appropriate. How might an insurance company use this information? What is the correct interpretation of the…

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, ŷ = bo + bịx, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1.5 2.5 3 4 4.5 Overall Grades 89 86 81 79 72 67 62 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.

ASAP!! NOW Mr. Ali runs a gift shop in the Defense area, Karachi. He advertises weekly in the local newspapers and is considering increasing his advertising budget. Before doing so, he decides to evaluate the past effectiveness of these advertisements. Five weeks are sampled, and the advertising expenses (in PKR) and sales volume for each is shown in the Table 2. Justify if any linearity exists between sales and advertising by finding correlation coefficient values. Develop a regression equation that would help Mr. Ali evaluate his advertising. Perform hypothesis test to check the significance of the results obtained in part b and c.   S.No. Sales (PKR 100) Advertising (PKR 100) 1.00 6.00 4.10 2.00 8.00 6.72 3.00 6.00 4.35 Table 2

Seven North American Green Frogs (Rana clamitans) had their jumping distance recorded (in mm) multiple times in a laboratory. The mean jumping distance for these frogs along with their length (measured from snout to vent in miMillimeters) are presented in the table below. Length of Frog 52 68 37 65 77 81 59 Mean Jumping Distance  546 673 415 659 793 814 563   (a) Determine the linear regression model that will best predit the mean jumping distance of a North American Green Frog based on the frog's length. (b) How well does the linear regression model fit this sample data? (c) Use the linear regression model to predict the mean jumping distance of a North American Green Frog that is 48 mm in length. No excel, please.

Look or search for a real-life problem or phenomenon in psychology and consider two important and related variables that have an existing data set. Then perform/ find the following:  a.Determine the linear correlation coefficient for the relationship between the two chosen variables. b.Interpret the strength of relationship for the two variables based on the calculated Pearson r value in (a). c.Find the prediction equation of the regression line for the data using the least squares method. d.Construct the scatter plot of the data and the regression line.

A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below:   Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer. How far can you rely upon this model? Or, what is the percentage variation in House explained by the model? What is the predicted house size (in hundreds of square feet) for an individual earning an annual income of IDR 400 million and having a family size of 4?