Hi,Please answer all parts.Thank you! CHALLENGE ACTIVITY2.3.2: Excel: Linear regression.Click this link to download the spreadsheet for use in this activity.Jump to level 1The famous iris dataset (the first sheet of the spreadsheet linked above) was first published in 1936 by RonaldFisher. The dataset contains 50 samples from 3 iris species: setosa, virginia, and versicolor. Four features aremeasured, all in cm: sepal length, sepal width, petal length, and petal width.What is the equation for the least square regression line where the independent or predictor variable is sepallength and the dependent or response variable is sepal width for iris setosa?y = Ex: 1.234I+ Ex: 1.234What is the predicted sepal width for iris setosa for a flower with a sepal length of 4.7?Ex: 5.12cmCheckNextFeedback?D-D- 5:31 O OAHG 84%44 - UnsavedfxABDEFGH12Sepal length Sepal width Species"Sepal width' by 'Sepal length33.2 Iris-versicolor46.43.2 Iris-versicolor56.93.1 Iris-versicolor3.565.52.3 Iris-versicolor376.52.8 Iris-versicolor2.585.72.8 Iris-versicolor6.33.3 Iris-versicolor2104.92.4 Iris-versicolor116.62.9 Iris-versicolor1125.22.7 Iris-versicolor132 Iris-versicolor3 Iris-versicolor2.2 Iris-versicolor0.5145.9156.1245166.12.9 Iris-versicolorSepal length175.62.9 Iris-versicolor186.73.1 Iris-versicolor195.63 Iris-versicolor205.82.7 Iris-versicolor216.22.2 Iris-versicolor225.62.5 Iris-versicolor235.93.2 Iris-versicolor246.12.8 Iris-versicolor256.32.5 Iris-versicolor266.12.8 Iris-versicolor276.42.9 Iris-versicolor286.63 Iris-versicolor296.82.8 Iris-versicolor306.73 Iris-versicolor312.9 Iris-versicolor325.72.6 Iris-versicolor335.52.4 Iris-versicolor345.52.4 Iris-versicolor355.82.7 Iris-versicolor3662.7 Iris-versicolor3 Iris-versicolor3.4 Iris-versicolor375.4386396.73.1 Iris-versicolor406.32.3 Iris-versicolor415.63 Iris-versicolor425.52.5 Iris-versicolor435.52.6 Iris-versicolor446.13 Iris-versicolor455.82.6 Iris-versicolor462.3 Iris-versicolor475.62.7 Iris-versicolor3 Iris-versicolor2.9 Iris-versicolor485.7495.7506.22.9 Iris-versicolor515.12.5 Iris-versicolor525.72.8 Iris-versicolor5354555657585960616263646566droSepal width

Name: Hi,Please answer all parts.Thank you! CHALLENGE ACTIVITY2.3.2: Excel:
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Description: Hi,Please answer all parts.Thank you! CHALLENGE ACTIVITY2.3.2: Excel: Linear regression.Click this link to download the spreadsheet for use in this activity.Jump to level 1The famous iris dataset (the first sheet of the spreadsheet linked above) was

The regression analysis is conducted using EXCEL. The software procedure is given below: Enter the…

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What is the equation for the least square regression line where the independent or predictor variable is sepal length and the dependent or response variable is sepal width for iris setosa? = Ex: 1.234 I+ Ex: 1.234 What is the predicted sepal width for iris setosa for a flower with a sepal length of 4.7? Ex: 5.12 cm

What is the equation for the least square regression line where the independent or predictor variable is sepal length and the dependent or response variable is sepal width for iris setosa? = Ex: 1.234 I+ Ex: 1.234 What is the predicted sepal width for iris setosa for a flower with a sepal length of 4.7? Ex: 5.12 cm

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
The operations manager of a musical instrument distributor feels that the demand for Bass Drums may be related to the number of television appearances by the popular rick group Green Shades during the previous month. The manager has collected the data shown in the following table. Demand for Bass Drums 3 6 7 5 10 8 Green Shades TV appearances 3 4 7 6 8 5 Develop the linear regression equation to forecast. Forecast demand for Bass Drums when Green Shades’ TV appearances are 10. Compute MSE and standard deviation for Problem 8.
Ch 13. 7: Refer to the Lincolnville School District bus data. First, add a variable to change the type of engine (diesel or gasoline) to a qualitative variable. If the engine type is diesel, then set the qualitative variable to 0. If the engine type is gasoline, then set the qualitative variable to 1. Develop a regression equation using statistical software with maintenance cost as the dependent variable and age, odometer miles, miles since last maintenance, and engine type as the independent variables.
It is considered that the number of employees in the enterprise affects the number of production. Data are given below. Which of the following is the simple linear regression equation?
QUESTION 2 XXX Electric Illuminating Company is doing a survey on the relationship between electricity used in kilowatt-hours (thousand) and the number of rooms in a private single-family residence. A random sample of 10 homes was selected and the electricity consumption recorded as below. ii. Find a suitable linear regression equation ? = ? + ??. iii. Determine the number of kilowatt-hours (thousand) for an eleven-room residence.
Suppose the Sherwin-Williams Company is interested in developing a simple regression model with paint sales (Y) as the dependent variable and selling price (P) as the independent variable. Complete the following worksheet and then use it to determine the estimated regression line. Sales Region Selling Price Sales ($/Gallon) (x 1000 Gal) ii xixi yiyi xixiyiyi xi2xi2 yi2yi2 1 15 160 2,400 225 25,600 2 13.5 220 2,970 182.25 48,400 3 16.5 140 2,310 272.25 19,600 4 14.5 190 2,755 210.25 36,100 5 17 120 2,040 289 14,400 6 16 160 2,560 256 25,600 7 13 210 2,730 169 44,100 8 18 150 2,700 324 22,500 9 12 220 2,640 144 48,400 10 15.5 190 2,945 240.25 36,100 Total 151 1,760 26,050 2,312 320,800 Regression Parameters Estimations Slope (ββ) -16.49 Intercept (αα) 424.98 In words, for a dollar increase in the selling price, the expected sales will increase by 2,640 gallons in a given sales region.…
Suppose the Sherwin-Williams Company is interested in developing a simple regression model with paint sales (Y) as the dependent variable and selling price (P) as the independent variable. Complete the following worksheet and then use it to determine the estimated regression line. Sales Region Selling Price Sales ($/Gallon) (x 1000 Gal) ii xixi yiyi xixiyiyi xi2xi2 yi2yi2 1 15 160 2,400 225 25,600 2 13.5 220 2,970 182.25 48,400 3 16.5 140 2,310 272.25 19,600 4 14.5 190 2,755 210.25 36,100 5 17 120 2,040 289 14,400 6 16 160 2,560 256 25,600 7 13 210 2,730 169 44,100 8 18 150 2,700 324 22,500 9 12 210 2,520 144 44,100 10 15.5 190 2,945 240.25 36,100 Total 151 1,750 2,312 What is the estimate of the standard deviation of the estimated slope (sbsb)? 2.627 3.173 2.877 Can you reject the hypothesis (at the 0.05 level of significance) that there is no relationship (i.e., β=0β=0) between the…
The use of multiple logistic regression is warranted when there are two or more independent quantitative or nominal variables and one dichotomous dependent variable. a. True b. False
A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below. a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result. The coefficient of determination is 1.___? (Round to three decimal places as needed.) This value indicates that 2.____ of the sample variation in the number of software millionaire birthdays is explained by the linear relationship with the total number of births in the region. (Round to one decimal place as needed.) b. Find the coefficient of determination for the simple linear regression model…
Question 4b. (b) Finally, the researcher is interested in examining the regression model for knowledge, attitudeand practices towards the COFLU-20. The following model was developed to forecastindividual practices towards COFLU-20 using knowledge and attitude scores.P = α + β K + δ Awhere P = Practice towards COFLU-20 scoreK = Knowledge towards COFLU-20 scoreA = Attitude towards COFLU-20 scoreThe data are processed using MINITAB and the output in Exhibit 1 below was obtained:Exhibit 1The regression equation is *************Predictor Coef SE t-ratio PConstant 4.755 0.462 10.282 0Knowledge 0.8 0.039 2.055 0.041Attitude 0.024 0.6 0.393 0.695R-sq = 81.5%Analysis of VarianceSOURCE DF SS MS F PRegression 2 38.06 19.03 2.284 0.104Error 297 2474.887 8.333Total 299 2512.947(i) Identify the dependent variable(s) and the independent variable(s).(ii) Is δ significant?Show proof of the testing process used to arrive at your decision.State what this means in terms of attitude and practices. (iii)…
Section 10.2 Question #5 Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. x 7 10 8 9 12 14 13 5 11 6 4 y 8.11 10.67 9.27 10.12 10.86 9.82 10.49 4.89 10.91 6.66 2.82 y= ____________+ _____________x (Round to two decimal places as needed.)