1.7. A study was conducted to analyse the effect of the size of the deposit level (X₂) and the likelihoodthat a returnable one-litre bottle will be returned. A return of a bottle recorded as Y = 1and noreturn Y = 0 was recorded.Use the following table to do a Pearson Chi-Square Goodness of Fit test in order to detect majordepartures from the logistic response function. That is that the logistic regression is a good fit forthe data.ft = [1 + exp(2.077 -0.136X]-¹Use x²(0.99, df) = 6.63 for a = 0.01, and determine the df-value.21030080ji | πιnjYjComplete the following table:123j=X₁41230050P₁ = Y/n Ojo not ret315300126E jo not retn - Ej1 retOj1 retChi Square22300150=Ej1 ret= n* πTotalsTotals Eo(Ojo - Ejo)²EjoTotals E₁(0₁₁ - Ej₁)²Ej₁

Given the table to do a Pearson Chi-Square of Fit test in order to detect major departures from the…

Answered: 1.7. A study was conducted to analyse…

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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A researcher is attempting to explore the relationship between study duration per day (0 - 8) and the passing of the science course (Pass=1, Fail=0). For this purpose, a sample of 36 students from Alabama University are chosen. Summary of the logistic regression model has been included. Please help me understand and choose from the following below, which is the correct representation of the related logistic regression model in the summary photo attached. - PASS = 0.57 + 3.69 X SDURATION - PASS= 3.69 +0.57 X SDURATION - PASS/FAIL=3.69 + 0.57 x SDURATION - P(PASS/F(FAIL)=3.69+ 0.57 X SDURATION - PASS/FAIL= 0.57 + 3.69 x SDURATION - ln{P(PASS/F(FAIL)}=0.57 + 3.69 X SDURATION - ln{P(PASS/F(FAIL)}= 3.69+ 0.57 X SDURATION
For a logistic regression looking at the log-odds of obesity among 20 to 70 year olds, age was included as a predictor. Age was recorded into categories: 20-29, 30-39. 40-49, 50-59, and 60-70. Given it is an ordinal variable, the statistician acknowledged that age could be included in the logistic regression model as continuous or categorical. Suppose that the statistician used loglikelihood ratio test of nested models to examine whether age could be treated as continuous. First, what is the null hypothesis? O Age is not a predictor of obesity Age is appropriate as a continuous variable O Age is appropriate as a categorical variable O Age is a predictor of obesity
To determine characteristics associated with Y = whether a cancer patient achieved remission (1 = yes), a study used logistic regression. The most important explanatory variable was a labeling index (LI) that measures proliferative activity of cells after a patient receives an injection of tritiated thymidine. It represents the percentage of cells that are “labeled.” Table 4.8 shows the grouped data. Software reports Table 4.9 for a logistic regression model using LI to predict π = P (Y = 1). A. Show how software obtained π ̂ = 0.068 when LI = 8. B. Show that π ̂ = 0.50 when LI = 26.0 D. The lower quartile and upper quartile for LI are 14 and 28. Show that π ̂ increases by 0.42, from 0.15 to 0.57, between those values. E. When LI increases by 1, show the estimated odds of remission multiply by 1.16
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 11,433.70 + 3312.68(Education) + 819.54 (Experience ) Suppose an employee with 7 years of education has been with the company for 5 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary? 囲 Tables E Keypad Answer Keyboard Shortcuts
If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?
Sam had 3 caffeinated drinks on april 24 Use linear regression to predict the number non-caffeinated drink he had on April 24 and calculate the error of estimate for your prediction.
Table 4.18 shows estimated effects for a fitted logistic regression model with squamous cell esophageal cancer (1 = yes, 0 = no) as the response variable Y. Smoking status (S) equals 1 for at least one pack per day and 0 other- wise, alcohol consumption (A) equals the average number of alcoholic drinks consumed per day, and race (R) equals 1 for blacks and 0 for whites. A. To describe the race-by-smoking interaction, construct the prediction equation when R = 1 and again when R = 0. Find the fitted YS conditional odds ratio for each case. Similarly, construct the prediction equation when S = 1 and again when S = 0. Find the fitted YR conditional odds ratio for each case. Note that, for each association, the coefficient of the cross-product term is the difference between the log odds ratios at the two fixed levels for the other variable. B. In Table 4.18, explain what the coefficients of R and S represent, for the coding as given above. What hypotheses do the P -values refer to for…
q12
Refer to the Mintab output below. What can be said based on this ouput (in other words, which statement is TRUE)? Group of answer choices a. Age_Years is signficant in the prediction of the output under consideration b. The results of this research analysis cannot be reliably interpreted c. This output is from a simple linear regression research analysis d. An analysis of the residuals is required e. Answers 1 through 4 are all true
The following table shows both current and projected data on the number, in millions, of Facebook users in a certain country. t = years since 2012 F = number of Facebookusers (millions) 0 9.7 1 10.5 2 10.8 3 12.2 4 13.1 5 13.2 6 13.8 (a) Plot the data. (b) Find the equation of the regression line. (Round regression line parameters to two decimal places.) F(t) = Add the graph of the regression line to the plot from part (a). (c) Explain in practical terms the meaning of the slope of the regression line. (Round your answers to two decimal places). The slope of the regression line is million users per year. The slope means that, from 2012 on, the number of Facebook users in the country is expected to increase by million each year.
3. A study of beer consumption using the annual data from 1980 -2001 produced the following regression: Y = 0.41 +0.052.X₁; −0.047X2; +0.032X3; −0.018X4i (0.027) (0.011) (0.009) (0.008) (0.009) R² = 0.94 F = 66.58 DW statistic=1.32, figures in the brackets are standard errors Where: Y₁ = Annual aggregate beer consumption in year t (billion pints) X₁₁ = Real disposable income income in year t ($ billions at 2001 prices) X2i = Price of beer in year t (index number with 2001 = 100) X31 Price of wine and sprits in year t (index number with 2001 = 100) = X4₁ = Price of cigarettes in year t (index number 2001 = 100) (a) What is the effect of a change in the price of beer on beer consumption? Does it have the correct sign? Explain. (b) Comment on the signs of the other three coefficients. Are they as expected? (c) Tests the significance of the coefficients on each of the variables Xit i = 1, 2, 3, 4. What assumptions have you made in carrying out these tests? (d) What conclusion do you draw…
Question 5 5.1. A study was conducted to ascertain the effectiveness of a treatment of diseased plants. A recorded value of Y = 1 indicates that the treatment is effective and a value of Y = 0 is recorded when no effect is noticed. The outcome of the effectiveness of the treatment considers the treatment dosage, the above ground biomass, the rate of growth and the height of a plant recorded as 1 if above average height and recorded 0 as below average height. The following partial data set is provided. EFFECTS (Y) DOSAGE X₁ 0.2138 0.3907 0.2138 1 1 0 The output for running the full logistic regression model is given below: Coefficients (Intercept) HEIGHT BIOMASS RATE DOSAGE Estimate -1.23 0.31 0.33 0.34 -1.86 BIOMASS X₂ RATE X₂ 0.0525 1.1 0.0264 1.2 0.0920 1.3 Estimate -1.25 0.21 Std. Error 0.6795 0.6821 5.9374 2.4112 2.1420 Log Likelihood: -2.579 The output for the reduced model is given below: Coefficients (Intercept) BIOMASS Std. Error 0.4129 4.7390 6 z value -1.653 0.456 2.594 3.167…

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