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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 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 Question What is the meant by the standard error of the estimate? Which number measures the scatter of points about the regression line?

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 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

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.

Please help me with question d, e, f, g, h, and I. Thank you.

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.

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.

The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?

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?

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.

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?

Please please how to write if based on the following

There is a dataset of size n = 51 and is for the 50 states and the District of Columbia in the United States. The dependent variable is year 2002 birth rate per 1000 females 18 to 19 years old and independent variable is the violent crime rate (per 1000 population). A simple linear regression model is run with the results given below. What is the Pearson correlation coefficient between x and y variables? The R squared of the model? What kind of relationship there is?

sample of trucks and their "static weight" and "weight in motion" weights are in thousands of pounds. What percent of the variability in static weight can be explained by the linear model rounded to the nearest 10th?

▼ Set background Clear frame 6.2 2) The program used to create this scatterplot found the line-of-best-fit and reported the r-squared value as r^2 = 0.805 for the relationship between arm-span and height for several individuals. What is the correlation coefficient? Is it positive or negative? Explain how you know. 1850- 1800- 1750- 1700- 00 1650- 1600- 1550- arm-span (cm) height (cm) 160.0 R Sq Linear-0805 : =S Open c 2 IN

3. You are working on a regression when accidentally you dump coffee all over yourregression output. Using the remaining values, find the missing values in each blank numbered 1-9 SUMMARY OUTPUT Regression Statistics Multiple R 0.860644193 R Square # 4 Adjusted R Square 0.72282625 Standard Error #5 Observations 32 ANOVA df MS F Significance F Regression 2 834.0726419 417.0363209 41.4216016 3.16178E-09 Residual #2 #1 # 3 Total 31 1126.047188 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 36.90833048 2.190798638 16.84697527 1.62066E-16 32.42764417 41.38901679 cyl -2.264693597 0.575889243 #7 0.000480375 -3.442519346 -1.086867847 hp #6 0.01500073 -1.274717745 0.212528465 #8 #9 Screenshot

Which of the variables is the indepenent variable and dependent variable for the following question. fit a simple linear regression model to predict latitudes using average monthly range lat= latitudes range= the average monthly range between mean montly maximum and minimum temperatures for a selected set of US cities.

What variables do Teczar find to have the most significant controlled association with women in ministries?

Only one can be chosen