This graph shows data on income and happiness from a sample. The fitted linear is based on a linear regression of happiness on income. According to this graph, which of the following is true? (Select all that apply.) happiness 6- 0- 4 income The slope of the regression line is negative. There is a positive relationship between income and happiness. More income causes more happiness. As income increases, people tend to be happier.

Simple linear regression model helps to adjust out the effects of confounding factors in a statistical analysis. A. TrueB. FalseC. None of the above choices

What is the dollar amount of change in sales for each new year according to the regression (slope) model?  Do not use commas or $ in your answer.

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Roller coasters get their speed as a result of dropping down a steep incline. The table below gives height of a drop and the speed achieved for different roller coasters around the world. Drop in Meters (m        25      30       28        58      55      40      90      100 Speed of the coaster (k.p.h)           80           90             78            93          90         93        105       128   Have your calculator find the linear regression for the roller coaster data above. Write the linear model, clearly define the variables and state correlation coefficient and clearly interpret the slope.                   Use the model to find the drop height that will result in a coaster speed of 150 kph.     Have your calculator find the exponential regression for this data. Write the exponential model, clearly define the variables and state correlation coefficient.       Use the model to find the drop height that will result in a…

Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion).  Use the horizontal axis for the customer age to graph.  Find the linear regression line that models the data by each type of customer.  Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales.  What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y  / Change in x

B-4 A toy store manager would like to know how the price can affect the sales of toys. In his data, he obtained 100 pairs of prices (unit: £) and volume of sales (unit: hundreds) for the toys. A simple linear regression was run with Sales as the dependent variable and Price as the independent variable with the following results. Regression Statistics R Square Adj. RSqr StErr of EST 0.876 0.767 0.701 4.184 Summary Table Variable Coeff. Std. Err Intercept 64.742 11.310 Price -2.645 0.602 (a) Write down the estimated regression equation. Explain this regression equation. (b) What's the value for R Square? Explain the meaning of this value. (c) Test the significance of the model at a=0.05 level. Write down the hypothesis you are testing, the statistic you use to evaluate the test, and your conclusion. (d) Use the regression output to construct a 99% confidence interval for the predicted sales when the price is set to be 15. (e) A colleague in the toy store claims that £1 increase in the…

Below is the actual data on the speed a person walks or runs in miles/hour and the calories the person burns per lap. Find the linear equation of best fit for this data. Compare the model to the data. Does the model seem to fit the data? Explain. What is the significance of the y-intercept? What is the slope and what does it mean? What is the x-intercept and what does it mean? What is the correlation coefficient and what does it mean? Speed (mph) Calories/lap 1 42 2 31 3 27 4 41 5 40 6 40 7 39 8 38 r answers to the nearest hundredth. The y-intercept means that if a person runs at ----------mph they will burn__________ per lap. The slope means that for each mph you increase your speed, you will burn an additional------------calories pay lap

Two variables  are defined, a regression equation is given, and one data point is given.    Study = number of hours spent studying for an exam                                                             Grade = grade on the exam                                                                                                                                                                                                                                                          GRADE= 41.0 + 3.7 (STUDY)                                                                                                                                                                                                                THE DATA POINT IS A STUDENT WHO STUDIED 10 HOURS AND RECIEVED AN 81 ON THE EXAM…

Which of the following statements best describes linear regression? A measure of the degree of dependence between two variables. A statistical technique using pairs of data and identifying a line which ‘best fits’ all of the pairs of observations. A measure of the proportion of changes in the dependent variable which can be explained by a change in the independent variable. Shows individual costs against their associated activity levels but makes no attempt to identify a linear relationship between them.