A regression was run to determine if there is a relationshipbetween hours of TV watched per day (x) and number ofsitups a person can do (y).The results of the regression are:Linear Regression Equation:Parameter EstimateIntercept 30.168Slope (TV-1.175Hours)Model SuymmaryStatisticCorrelationCoefficient rCoefficient ofDetermination ²Value-0.9640.929296Assume the correlation is significant (p-value < a), and usethis to predict the number of situps a person who watches12.5 hours of TV can do (to one decimal place)

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Answered: A regression was run to determine if…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 2) The slope is 2.521 and therefore differs from 0. 3) The slope significantly differs from 0. 4) Not enough evidence was found to conclude the slope differs significantly from 0. 5) The slope is equal to 0.
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. Explain clearly what this slope says about the change in the mean of y for a change in x.
A researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 4.5+ 14.4x Coefficients (Intercept) x Estimate 4.5 Ho: B₁ = 0 H₁: B₁ > 0 Ho: B₁ = 0 Ha: B₁ <0 14.4 Ho: B₁ = 0 Ha: B₁ #0 Std. Error Test statistic 2.9 4.7 1.55 3.06 P-value Specify the null and the alternative hypotheses that you would use in order to test whether a linear relationship exists between x and y. 0.07 0
Which of the following tools is not appropriate for studying the relationship between two numerica variables? Correlation coefficient Scatter plot Historam Regression
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 562 pounds? Use a significance level of 0.05. Chest_size_(inches) Weight_ (pounds)58 41450 31265 49959 45059 45648 260 What is the regression equation?^y = ____ + _____ x (round to one decimal place as needed.)What Is the best predicted weight of a bear with a chest size of 63 inches? ^y =____ pounds (round one decimal as needed)Is the result close to the actual weight of 452 pounds?(a) This result is very close to the actual weight of the bear.(b) This result is close to the actual weight of the bear.(c) This result is exactly…
Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1
From the results shown above, write the regression equation.
A regression of infant mortality rates (annual deaths per hundred births) on median annual household income (in thousands of dollars) is performed on data collected from 40 counties. The results are summarized as follows. Intercept: Slope: 18.46 -0.14 Sum of df Mean Squares Square Regression Residual Total 2.4 4.5 (a) Fill in the blanks in the ANOVA table (b) What is the predicted infant mortality rate in a county where the median household income is $40,000? (c) In words, what is the meaning of the coefficient -0.14? Do NOT simply say that this is the slope or the regression coefficient. Indicate what it means and how it can be interpreted.
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?
What does the slope and the intercept mean in this setting?
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A regression was done for 20 cities with the latitude (Latitude), a measurement of the distance of a location on the Earth from the equator in degrees and the average January temperatures (Temp) as the dependent variable measure in. The regression equation is a) The value of the slope is [Select ] [Select] TEMP=48.4 -0.31 x LATITUDE b) The predicted average January temperatures for the 20 cities for a latitude of 45 degrees is [Select] ✓. If in fact the [Select] average January temperature for a latitude of 45 degrees is 40° F , the residual is [Select] which is a(n) [Select] which means c) The coefficient of determination is R²: 66.2%. This value means [Select] The correlation r, is which means [Select] d) The y-intercept is [Select] the problem interprets to [Select] make sense in the context of the problem? [Select] Formulas: r = ±√R² Residual actual y - predicted y 0.7<|rl≤1 very strong 0.5

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