Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K) ID Marital Refund Тахable Cheat Status Income 1 Single Yes 125K No 2 Married No 100K No 3 Single No 70K No 4 Married Yes 120K No Divorced No 95K Yes Married No 60K No 7 Divorced Yes 220K No 8 Single No 85K Yes Married No 75K No 10 Single No 90K Yes 00
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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?Which best describes the null hypothesis associated with an Analysis of Variance (ANOVA)? Group of answer choices a. Ho: Variance 1 = Variance 2 = Variance 3 b. Ho: Standard Deviation 1 = Standard Deviation 2 = Standard Deviation 3 c. Ho: Proportion 1 = Proportion 2 = Proportion 3 d. Ho: Median 1 = Median 2 = Median 3 e. Ho: Mean 1 = Mean 2 = Mean 3
- Quarterly sales of a local department store over a 4-year period are given below: Quarters 2016 Q1 Sales (in millions) 500 Quarters 2018 Q1 Sales (in millions) 560 2016 Q2 490 580 2018 Q2 2018 Q3 2018 Q4 2019 Q1 2019 Q2 2016 Q3 570 630 2016 Q4 2017 Q1 2017 Q2 750 720 480 540 500 590 2017 Q3 580 2019 Q3 620 2017 Q4 740 2019 Q4 790 The data shows sign of seasonality. By computing seasonal indices for each quarter, forecast the sales for each quarter in year 2020, given that the estimated sales in 2020 isIs decision tree based on a linear combination of parameters ?year quarterly sales (000 units) Q1 Q2 Q3 Q4 2016 1300 1500 1200 2000 2017 1600 1800 1100 2200 2018 1700 1900 1300 2300 2019 1800 2100 1400 2500 Using a simple regression analysis, determine the trend equation of the sales and use it to estimate the number of units of clothing sold throughout the fiscal year 2020. Assume that Q1 of 2016 is 1, Q2 of 2016 is 2, etc. Show all relevant cakculation detail
- can you explain three outpots of coNNNNONication martris model with some examples ?A sample of twenty automobiles was taken, and the miles per gallon (MPG), horsepower, and total weight were recorded. Develop a linear regression model to predict MPG using horsepower as the only indepen- dent variable. Develop another model with weight as the independent variable. Which of these two models is better? Explain. MPG 44 44 40 37 37 34 35 32 30 28 26 26 25 22 20 21 18 18 16 16 4 HORSEPOWER 67 50 62 69 66 63 90 99 63 91 94 88 124 97 114 102 114 142 153 139 WEIGHT 1,844 1,998 1,752 1,980 1,797 2,199 2,404 2,611 3,236 2,606 2,580 2,507 2,922 2,434 3,248 2,812 3,382 3,197 4,380 4,036Consider the following quarterly demand level for electricity (in 1000 megawatts) in Mankweng from 2018 to 2022: 2018 2019 2020 2021 2022 Jan – March 21 35 49 60 10 - Apr – June 42 54 55 64 05 July – Sept Oct – Dec 60 91 95 99 12 14 74 80 1. Find the least squares trend line for electricity demand using Exx=0 method. 2. Find the seasonal index for each quarter. 3. Find adjusted seasonal index. 4. Find the De-seasonalized values for Quarter 1, 2,3 and 4 for the year 2023.
- Attendance at Orlando's newest Disneylike attraction, Lego World, has been as follows Quarter Guests (in thousands) Quarter Summer Year 2 Winter Year 1 58 99 Fall Year 2 149 Winter Year 3 Spring Year 1 Summer Year 1 Fal Year 1 Winter Year 2 75 Spring Year 3 63 Summer Year 3 Spring Year 21 82 Fall Year 3 Based on the given attendance, the seasonal indices for each of the seasons are (round your responses to three decimal places) Season Index Winter Spring Summer Fall 0000 Guests (in thousands) 125 54 99 146 210 993. Quarterly sales of a car dealer exhibit an almost constant mean over time, but sales fluctuate depending on the quarter of the year (exhibits seasonality). Quarter Q1 - Spring Q2 - Summer Q3 - Fall Q4 - Winter Mean Year 1 32 74 53 22 45.25 Year 2 30 71 63 15 44.75 Year 3 35 83 45 19 45.5 (a) Using three years' worth of quarterly data that is provided, develop a set of seasonal factors that could be used to forecast car sales (b) Forecast car sales in each of the four quarters (Q1-Q4) of the following year (Year 4), using these seasonal factors.Figure 1 shows the general discriminatory test for health status prediction. "Perfect test cut-off True negative; TN (specificity) True positive; TP (sensitivity) False negative (FN) False positive (FP) Healthy Sick (a) Test cut-off Moving cut-off to left reduces false negatives (higher specificity) at cost of reduced sensitivity Moving cut-off to right reduces false positives (higher sensitivity) at cost of reduced specificity TN TP FN FP Healthy Sick (b) Figure 1. The Discriminatory Test with (a) A Good Discrimination Test Cut-off, and (b) A Bad Discrimination Test Cut-off i. What is your next action, if you obtain a perfect discriminatory test result (where both false negative and false positive rate are near zero) during model training? Justify your action. ii. In which phase of machine learning methodology do you move the test cut-off point as shown in Figure 1(b)? Justify why you should do that.