completion 55 59 60 64 66 interception 4.5 3.5 3 2.5 1.5  Find the value of the coefficient of determination.

Use a = 0.01 and conduct a goodness of fit test using 5 classes to see whether the following sample appears to have been selected from a normal probability distribution. (Note: x= 70 and s= 18.) 55 86 94 58 55 95 55 52 69 95 90 65 87 50 56 55 57 98 58 79 92 62 59 88 40 Find the value of the test statistic. Find the p-value. (Round your answer to four decimal places.) p-value =

Ariel was running analyses over and over in census data and came across a correlation between weight and debt (r=.78). Independent variable is weight and dependent variable is debt. b) Curious Ariel noted some statistics on these weight (M=160lb, SD=15lb) and debt (M=196k, SD=20k). If Ariel wanted to calculate a regression equation, what would her slope and intercept be?

The table below gives the completion percentage and interception percentage for five randomly selected NFL quarterbacks. Based on this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for using the completion percentage to predict the interception percentage for an NFL quarterback. 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. completion 55 59 60 64 66 interception 4.5 3.5 3 2.5 1.5    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 y^.

The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, 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    Bone Density35    35043    34053    33954    32155    310 Step 4 of 6 :   Determine the value of the dependent variable yˆ at x=0.

The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY - 3.17 +0.823HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2700 lb, it has an engine displacement of 1.6 L, and its highway fuel consumption is 35 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.). Regression Table I R² Adjusted R2 WT/DISP WT/HWY Predictor (x) Variables P-Value WT/DISP/HWY 0.000 0.942 0.000 0.748 0.000 0.942 0.000…

Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂  = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…

If you have 2 datasets on which you have conducted a regression analysis, how would you determine whether you can combine them into one dataset?

A researcher studied the relationship between the amount of carbohydrates (x) and the amount of salt (y) in different food products. sample of 36 food products yielded these calculations: r = -0.1245, ŷ=0.5545– 0.1636x According to the correlation, if a food contains more carbohydrates, is it likely to contain more salt or less salt? Why or why not?

Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported asy=−96.07x+69.42y=-96.07x+69.42and the r=−0.27r=-0.27.What percentage of the variation in y can be explained by the variation in the values of x?r² = % (Report exact answer, and do not enter the % sign)