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I have no idea how to fill in the blank in this regression output... please help

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 7. Level of Significance = 0.1 8. Critical Value= 0.378419   Question : Based on the linear correlation coefficient (r) in Line 1, the variables(X,Y) are positively correlated   True False

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 Question What is meant by SSR? Which number measures the variation explained by the regression line?   Sum of the Squares Regression (SSR); amount of variation in Y explained by the variation in X explanatory variable   Sum of the Squares Regression (SSR); amount of variation in X explained by the variation in Y   SSR is the percent variation in total variation SST that is explained by the variation in X or SSR/SST = 5182.41/8208.90 = 0.631319 or 63%   Both A and C

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 10. t test = 0.794556 Question What is meant by a hypothesis? State the hypothesis in this example?   A hypothesis is a claim about the correlation between the Y and X variables in the population under study: There are two hypothesis (claims) Null Hypothesis is that (rho) = 0 Alternative Hypothesis is (rho) is not equal to 0   A hypothesis is a claim about the correlation between the Y and X variables in the population under study: There is only one hypothesis (claim) as follows: Null Hypothesis is that (rho) = 0   A…

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 10. t test = 0.794556 Question What is meant by a hypothesis? State the hypothesis in this example? A hypothesis is a claim about the correlation between the Y and X variables in the population

Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 Question What is the meant by the standard error of the estimate? Which number measures the scatter of points about the regression line?

Why is the equation of the regression line for this scatter plot ?

The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood Pressure SE 20986 339.28 Age Blood sugar 209.2 Blood pressure 207.2 S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5% Coef Constant Analysis of Variance Source DF Regression 3 Residual Error. 4 Total 7 SS Coef 2912 71.95 179.3 225.4 23863180 3703570 27566750 T 7.21 4.72 * 0.92 MS 7954393 925892 F *** P 0.002 0.009 0.308 ** P 0.032 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.

В. A model estimated using a dataset with 125 observations generates the following results. SS df MS Regression 919587.543 4 229896.9 Error 2590390.62 121 534.2113 Std. Variable B Error t P>lt| X2 -0.0126355 0.005519 -2.28937 0.022 X3 0.5957923 0.014482 41.13934 0.000 Х4 1.124589 0.877192 1.282032 0.200 X5 0.3237421 0.060709 5.332661 0.000 constant 8.86016 1.766116 5.016749 0.000 What is the R2 for this sample? What information does the R² provide?

Is the y- intercept meaningful or meaningless?

What is a numerical prediction from the regression line equation shown in the photo?

Drug use The 2011 World Drug Report investigated theprevalence of drug use as a percentage of the populationaged 15 to 64. Data from 22 European countries are shown in the following scatterplot and regression analy-sis. (Source: World Drug Report, 2011. www.unodc.org/ unodc/en/data-and-analysis/WDR-2011.html) Dependent variable is CocaineR-squared = 38.1%s = 0.724 with 22 - 2 = 20 degrees of freedomVariable Coefficient SE(Coeff) t-Ratio P-ValueIntercept 0.35707 0.2757 1.295 0.21Cannabis% 0.14264 0.0406 3.512 0.002a) Explain in context what the regression says.b) State the hypothesis about the slope (both numericallyand in words) that describes how use of marijuana isassociated with other drugs. c) Assuming that the assumptions for inference are satis-fied, perform the hypothesis test and state your conclu-sion in context. d) Explain what R-squared means in context.e) Do these results indicate that marijuana use leads tothe use of harder drugs? Explain.

5) Can i get help with this question please

A researcher’s results are shown below using Femlab (labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 U.S. states.   Regression Statistics Multiple R 0.313422848   R Square 0.098233882   Adjusted R Square 0.079447088   Standard Error 32.07003698   Observations 50       Variable Coefficients Standard Error t Stat Intercept   343.619889     61.0823514     5.62552   Femlab   –2.2833659     0.99855319     –2.28667     Which statement is valid regarding the relationship between Femlab and Cancer? Multiple Choice   At the .05 level of significance, there isn’t enough evidence to say the two variables are related.   This model explains about 10 percent of the variation in state cancer rates.   If your sister starts working, the cancer rate in your state will decline.   A rise in female labor participation rate will cause the cancer rate to decrease within a state.

Q1 Data displayed in this graph were standardized. True or false? Q2 Based on this graph, one can accurately estimate shoe size heritability to be 0.8676. True or false?

10)   A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)

A student collected concentration versus absorbance data for a series of standards and produced a standard curve. Which value of 2 would reflect the linear regression curve the student produced? Absorbance 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 ² = 0.10 ² = 0.76 ² = 0.98 2 = 0.45 1 Absorbance vs. Concentration 2 3 Concentration (ppm) 4 5 6

To investigate the relationship between the milage and sales price for a popular car model the pictured scatterplot was used. a) Based on the excel output that's pictured, what is the estimated regression equation that could be used to predict the price given the miles? b) Does the model fit the data? (ie whether the regression relationship is statistically significant) Did the estimated regression equation provide a good fit? (ie use the coefficient of determination to explain variability independent variable) c) Suppose you are considering purchasing a car of this model with 60000 miles. Using the estimated regression equation, predict the price.

Q2 Part b

Multiple regression analysis was used to study the relationship between a dependent variable, y, and four independent variables; x1, x2, x3, and x4. The following is a partial result of the regression analysis involving 31 observations.       Coefficients Standard Error     Intercept 18.00 6.00     x1 12.00 8.00     x2 24.00 48.00     x3 -36.00 36.00     x4 16.00 2.00     ANOVA   df SS MS F Regression     125   Error         Total   760       Compute the multiple coefficient of determination. Perform a t test and determine whether or not β1 is significantly different from zero (α = .05). Perform a t test and determine whether or not β4 is significantly different from zero (α = .05). At α = .05, perform an F test and determine whether or not the regression model is significant.

A) Which point from the data has the largest residual? B) Explain what the residual means in context. Is this point an outlier? An influential point? The residual means that when the swim time is______, the observed heart rate is about  _____  beats less than the predicted rate. When this point is removed, it has an effect on the regression line, so it is influential. The point is not an outlier, because the residual is less than twice the standard deviation.

mean of x = 2.882, standard deviation = 1.634  Mean of y = 2.588, standard deviation = 0.246  The correlation coefficient is 0.159 slope coefficient for regression line = .0239 y intercept = 2.52 write the regression model

What variables do Teczar find have the most significant controlled associations with women in national parliaments?

/was/ui/v2/assessment-player/index.html?launchid=831489a9-d255-4984-8d47-074c80bc17d6#/question Problems Question 2 of 8 Two variables are defined, a regression equation is given, and one data point is given. Weight Training = Weight = = maximum weight capable of bench pressing (pounds) number of hours spent lifting weights a week The data point is an individual who trains 5 hours a week and can bench 150 pounds. 99 + 11.7(Training) (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Residual = i II Predicted value = eTextbook and Media 3' " lbs lbs C D 6 O W

estion 3 of 38 university and gathers their freshman year GPA data and the high school SAI score reported on each of their college applications. He produces a scatterplot with SAT scores on the horizontal axis and GPA on the vertical axis. The data has a linear correlation coefficient of 0.506701. Additional sample statistics are summarized in the table below. Variable Sample Sample standard Variable description mean deviation high school SAT score x 1504.291401 Sx = 105.782904 %3D y freshman year GPA y = 3.240805 Sy = 0.441205 r = 0.506701 slope 0.002113 Determine the y-intercept, a, of the least-squares regression line for this data. Give your answer precise to at least four decimal places. tems of use Thelp about us குசங் careers 2:30 PM 10/24/20 o耳 国 @ hp