Chapter 2A - Solution Manual

# Chapter 2A - Solution Manual - Chapter 02A Least-Squares...

This preview shows pages 1–6. Sign up to view the full content.

Chapter 02 A Least-Squares Regression Computations Appendix 2A Least-Squares Regression Computations Exercise 2A-1  (20 minutes) 1. Month Rental Returns  (X) Car Wash Costs  (Y) January. .......... 2,310 \$10,113 February. ........ 2,453 \$12,691 March. ............. 2,641 \$10,905 April. ............... 2,874 \$12,949 May. ................ 3,540 \$15,334 June. ............... 4,861 \$21,455 July. ................ 5,432 \$21,270 August. ............ 5,268 \$19,930 September. ..... 4,628 \$21,860 October. .......... 3,720 \$18,383 November. ...... 2,106   \$9,830 December. ...... 2,495 \$11,081 The least-squares regression results are as follows: Intercept (fixed cost). .................. \$2,296 Slope (variable cost per unit). ..... \$3.74 R 2 ............................................... 0.92 Therefore, the cost formula is \$2,296 per month plus \$3.74 per  rental return or: Y = \$2,296 + \$3.74X Note that the R 2  is 0.92, which means that 92% of the variation in  glazing costs is explained by the number of units glazed. This is a  very high R 2  and indicates a very good fit. 2A-1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Chapter 02 A Least-Squares Regression Computations Exercise 2A-1  (continued) While not a requirement of the exercise, it is always a good to plot the  data on a scattergraph. The scattergraph can help spot nonlinearities  or other problems with the data. In this case, the regression line  (shown below) is a reasonably good approximation to the relationship  between car wash costs and rental returns. 2A-2
Chapter 02 A Least-Squares Regression Computations 2A-3

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Chapter 02 A Least-Squares Regression Computations Exercise 2A-2  (30 minutes) 1. Week Units  (X) Total Glazing Cost  (Y) 1 8 \$270 2 5 \$200 3 10 \$310 4 4 \$190 5 6 \$240 6 9 \$290 The least-squares regression results are as follows: Intercept (fixed cost). .................. \$107.50 Slope (variable cost per unit). ..... \$20.36 R 2 ............................................... 0.98 Therefore, the cost formula is \$107.50 per week plus \$20.36 per  unit or: Y = \$107.50 + \$20.36X Note that the R 2  is 0.98, which means that 98% of the variation in  glazing costs is explained by the number of units glazed. This is a  very high R 2  and indicates a very good fit. 2. Y = \$107.50 + \$20.36X 3. Total expected glazing cost if 7 units are processed: Variable cost: 7 units × \$20.36 per unit. .................. \$142.52 Fixed cost. ...............................................................   107.50     Total expected cost. ................................................ \$250.02 2A-4
Chapter 02 A Least-Squares Regression Computations Problem 2A-3  (45 minutes) 1.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 12/14/2011 for the course ACCOUNTING 2301 taught by Professor Sarah during the Fall '10 term at HCCS.

### Page1 / 14

Chapter 2A - Solution Manual - Chapter 02A Least-Squares...

This preview shows document pages 1 - 6. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online