Calculate the value of the sample correlation coefficient. (Round your answer to three decimal places.) ___________   (b) If you decided to fit the simple linear regression model to this data, what proportion of observed variation in maximum prevalence could be explained by the model relationship? (Round your answer to three decimal places.) ____________   (c) If you decided to regress UV transparency index on maximum prevalence (i.e., interchange the roles of x and y), what proportion of observed variation could be attributed to the model relationship? (Round your answer to three decimal places.) _____________   (d) Carry out a test of H0: ? = 0.5 versus Ha: ? > 0.5  using a significance level of 0.05. [Note: The article reported the P-value for testing H0: ? = 0  versus H0: ? ≠ 0.]  (Round your test statistic to two decimal places and your P-value to four decimal places.) ___________

How do we do in excel

The following table shows unit sales of iPhones for the given year.  t = years since 2010 0 1 2 3 I – units sold in millions 8 16 37 47   Find the equation of the regression line. ________________________   How many units sold does the regression line predict for 2015?   ________________________   Explain in practical terms the meaning of the slope.

1. Question: Malaria is a leading cause of infectious disease and death worldwide. It is also a popular example of a vector-borne disease that could be greatly affected by the influence of climate change. Table 1 is a summary from a linear regression that uses dewpoint (°C) to predict malaria prevalence in West Africa.Fig. 1: Regression(a) Write the equation of the least square regression line. (b) Find the correlation coefficient r. (c) IsthereastrongcorrelationbetweendewpointandmalariaprevalenceinWestAfrica? (d) Is there a negative association between dewpoint and malaria prevalence in West Africa?

A statistical program is recommended. Consider the following data for a dependent variable y and two independent variables, x1 and x2. x1 x2 y 30 12 94 47 10 108 25 17 112 51 16 178 40 5 94 51 19 175 74 7 170 36 12 117 59 13 142 76 16 211 (a) Develop an estimated regression equation relating y to x1. (Round your numerical values to one decimal place.) y=_______________.   Predict y if x1 = 51. (Round your answer to one decimal place.) (b) Develop an estimated regression equation relating y to x2. (Round your numerical values to one decimal place.) ŷ =____________   Predict y if x2 = 16.(Round your answer to one decimal place.) c) Develop an estimated regression equation relating y to x1 and x2.(Round your numerical values to one decimal place.) ŷ =______________.  Predict y if x1 = 51 and x2 = 16.(Round your answer to one decimal place.) _________.

The goal of the simple regression analysis is to understand[Mark one answer]:_______ the nature of the association between the two numerical variables X and Y. _______ the nature of the association between the two categorical variables X and Y. _______ the nature of the linear association between the two numerical variables X and Y. _______ the nature of the linear association between the two categorical variables X and Y.

3. Regression analysis breaks scores on the DV into... (explain and give equations)

Question #8 Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line.   x 12 6 10 13 11 5 8 14 9 7 15   y 14.29 6.91 13.15 14.37 13.88 4.53 10.69 14.11 12.08 8.97 13.53     y= ________ + ________ x (Round to two decimal places as​ needed.)   Create a scatterplot of the data. Choose the correct graph below. ( Grapghs are attached)    Identify a characteristic of the data that is ignored by the regression line.   A. There is no trend in the data. B. There is an influential point that strongly affects the graph of the regression line. C. The data has a pattern that is not a straight line. D. There is no characteristic of the data that is ignored by the regression line.

Question 13 Which of the following statements is true about r2 in a simple linear regression model?     It is also called the coefficient of determination.     It represents the percent of variation in X that is explained by Y .     It represents the percent of variation in the error that is explained by Y .     It ranges in value from -1 to + 1.     It is also called the coefficient of correlation.     Question 14 The multiple regression model includes several dependent variables.  True  False