Topic 2 Tutorial Solutions
PREPARE BEFORE CLASS
1. Ch 19, page 856, Exercise 19.2.
The asset section of the statement of financial position and notes thereto of Megabus
Ltd is shown below:
Cash assets
Receivables
Inventories
Prepaid insurance
Furniture an

140.
Consider the following partial computer output from a simple linear regression analysis.
S = 0.4862 R-Sq = 0.7286
Analysis of Variance
What is the correlation coefficient?
-0.8536
Feedback: r2 = Explained variance/Total variance = 8.251/11.324 = .728

123.Consider the following partial computer output from a simple linear regression analysis.
Write the equation of the least squares line.
124.Consider the following partial computer output from a simple linear regression analysis.
Test H0:1 0 vs. Ha: 1>

116.
A data set with 7 observations yielded the following. Use the simple linear regression model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Find the rejection point for the t statistic (
= .05). Test H0: 1 0 vs. Ha: 1 > 0.
13.993, reject nul

Learning Objective: 13-01 Explain the simple linear regression model.
Topic: Simple Linear Regression
121.
Consider the following partial computer output from a simple linear regression analysis.
What is the estimated y-intercept?
-28.13
AACSB: Analytic
B

Blooms: Application
Bowerman - Chapter 13 #134
Difficulty: Easy
Learning Objective: 13-01 Explain the simple linear regression model.
Topic: Simple Linear Regression
135.
Consider the following partial computer output from a simple linear regression analy

74. Regression Analysis
The local grocery store wants to predict the daily sales in dollars. The manager believes that the
amount of newspaper advertising significantly affects the store sales. He randomly selects 7 days
of data consisting of daily grocer

112.
A local tire dealer wants to predict the number of tires sold each month. He believes that the
number of tires sold is a linear function of the amount of money invested in advertising. He
randomly selects 6 months of data consisting of monthly tire s

96. A local tire dealer wants to predict the number of tires sold each month. He believes that the
number of tires sold is a linear function of the amount of money invested in advertising. He
randomly selects 6 months of data consisting of tire sales (in

77.
An experiment was performed on a certain metal to determine if the strength is a function of
heating time. Results based on 10 metal sheets are given below. Use the simple linear
regression model.
= 30
= 104
= 40
= 178
= 134
Find the estimated y inter

65. The following results were obtained from a simple regression analysis:
= 37.2895 - (1.2024)X
r2= .6744 sb= .2934
For each unit change in X (independent variable), the estimated change in Y (dependent variable)
is equal to:
66. The following results we

63.
The _ is the proportion of the total variation in the dependent variable
explained by the regression model.
A. Coefficient of determination
B. Correlation coefficient
C. Slope
D. Standard error
AACSB: Reflective Thinking
Blooms: Knowledge
Bowerman - C

85.
An experiment was performed on a certain metal to determine if the strength is a function of
heating time. The simple linear regression equation is
= 1 + 1X. The time is in minutes and
the strength is measured in pounds per square inch.
The 95% confid

Accounting Concepts and Methods
Measurement of profit; Income statement;
Statement of Cash Flows; and
Analysis and interpretation of the statements
Assessments updates
Research assignment:
Week 3: Groups are finalised in tutorial classes this week;
Wee

Accounting Concepts and Methods (M)
Overview
THE UNIVERSITY OF ADELAIDE
Page 1 of 8
BUSINESS SCHOOL
MASTERS PROGRAMS
ACCTING 7019 ACCOUNTING CONCEPTS AND METHODS (M)
Topic 1:
Overview of Accounting:
Accounting process, decision making process, regulatory

Accounting Concepts and Methods (M)
Statement of financial position
THE UNIVERSITY OF ADELAIDE
Page 1 of 11
BUSINESS SCHOOL
MASTERS PROGRAMS
ACCTING 7019 ACCOUNTING CONCEPTS AND METHODS (M)
Topic 2:
Balance sheet (Statement of financial position);
Introdu

60.
The degrees of freedom error (within group variation) of a completely randomized design
(one-way) ANOVA test with 4 groups and 15 observations per each group is:
56
Feedback: Error df = n - treatment levels = 60 - 4 = 56
AACSB: Analytic
Blooms: Applic

Topic: One-way ANOVA
106.
Suppose you are a researcher investigating the annual sales differences among five categories
of businesses. Looking at a total of 55 companies equally divided among categories groups A,
B, C, D, and E.
Is there a significant dif

Learning Objective: 11-02 Compare several different population means by using a one-way analysis of variance.
Topic: One-way ANOVA
73.
Consider the randomized block design with 4 blocks and 3 treatments given above.
What are the degrees of freedom for tre

73.
Regression Analysis
The local grocery store wants to predict the daily sales in dollars. The manager believes that
the amount of newspaper advertising significantly affects the store sales. He randomly selects
7 days of data consisting of daily grocer

100.
A local tire dealer wants to predict the number of tires sold each month. He believes that the
number of tires sold is a linear function of the amount of money invested in advertising. He
randomly selects 6 months of data consisting of tire sales (in

81.
An experiment was performed on a certain metal to determine if the strength is a function of
heating time. Results based on 10 metal sheets are given below. Use the simple linear
regression model.
= 30
= 104
= 40
= 178
= 134
Find the t statistic and t

96.
A local tire dealer wants to predict the number of tires sold each month. He believes that the
number of tires sold is a linear function of the amount of money invested in advertising. He
randomly selects 6 months of data consisting of tire sales (in

139.Consider the following partial computer output from a simple linear regression analysis.
S = 0.4862 R-Sq = _
Analysis of Variance
What is the coefficient of determination?
140.Consider the following partial computer output from a simple linear regress

Chapter5:
Thecompetitivefirm
Preparedby
Taha Chaiechi
JamesCookUniversity
and
MartineMariotti
ANU
Learningobjectives
Inthislectureyouwilllearn:
whatdeterminesthelevelofoutputafirmwill
supplyatanygivenprice
whatdetermineswhetherafirmentersorexitsa
market

Chapter13:
Aggregatedemandand
inflation
Preparedby
Taha Chaiechi
JamesCookUniversity
and
MartineMariotti
ANU
Learningobjectives
Inthislectureyouwilllearn:
howtherealinterestrateisdeterminedbythe
interactionofsavingandinvestmentthroughthe
capitalmarket
t

Chapter15:
Stabilisationpolicies
Preparedby
Taha Chaiechi
JamesCookUniversity
and
MartineMariotti
ANU
Learningobjectives
Inthislectureyouwilllearn:
themodernviewsofthemacroeconomictradeoffs
thatpolicymakersface
therolesofautomaticstabilisersanddiscretio

Chapter12:
Introductiontobusiness
cycles
Preparedby
Taha Chaiechi
JamesCookUniversity
and
MartineMariotti
ANU
Learningobjectives
Inthislectureyouwilllearn:
howeconomistsdescribeeconomicfluctuations
whyeconomiesexperiencefluctuationsin
productionandemplo

Chapter8:
Governmentpoliciestowards
competition
Preparedby
Taha Chaiechi
JamesCookUniversity
and
MartineMariotti
ANU
Learningobjectives
Inthislectureyouwilllearn:
whydeparturesfromperfectcompetitionare
undesirable
whatpoliciesAustraliangovernmentshavepu

Chapter3:
Elasticity
Preparedby
Taha Chaiechi
JamesCookUniversity
and
MartineMariotti
ANU
Learningobjectives
Inthislectureyouwilllearn:
whatismeantbytheconceptofelasticity
howelasticityhelpsexplaintheeffectsonpricesand
quantitiesofshiftsindemandandsuppl

108.
A local tire dealer wants to predict the number of tires sold each month. He believes that the
number of tires sold is a linear function of the amount of money invested in advertising. He
randomly selects 6 months of data consisting of tire sales (in

80. An experiment was performed on a certain metal to determine if the strength is a function of
heating time. Results based on 10 metal sheets are given below. Use the simple linear regression
model.
= 30
= 104
= 40
= 178
= 134
Determine the standard err

131.Consider the following partial computer output from a simple linear regression analysis.
S = 0.4862 R-Sq = _
Analysis of Variance
Write the equation of the least squares line.
132.Consider the following partial computer output from a simple linear reg

69.
Regression Analysis
The local grocery store wants to predict the daily sales in dollars. The manager believes that
the amount of newspaper advertising significantly affects the store sales. He randomly selects
7 days of data consisting of daily grocer

90.
An experiment was performed on a certain metal to determine if the strength is a function of
heating time. Partial results based on a sample of 10 metal sheets are given below. The simple
linear regression equation is
= 1 + 1X. The time is in minutes