AIC

Australian Business Statistics



Australian Business Statistics

Introduction

In this paper we consider the relationship between profit and inflation movements in the Australian manufacturing sector. Our goal is to determine whether there is a clearly defined long-term relationship between profit and inflation, and that influence short-term (cyclical) changes in the supply relationship. Pricing is most commonly associated with an abbreviated form of an equation models seen various Phillips curve. Phillips curve, the relationship between inflation and the inflation of inflation or, after some manipulation, the relationship between inflation and its own lagged values are expressed. In what form is presented, the basic model is assumed, that is after taking into account the inflation of transportation or autoregressive structure of the process there is a stable relationship between inflation and some measure of excess demand or supply. This study focuses on the nominal yield that much of the literature, the relationship between real yields and inflation are discussed. Both aspects are important in analyzing the effects of inflation.

Analysis

Frequency distribution for the variable profit before tax:

profit before income tax (Binned)

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

<= 156

1

.5

.5

.5

157 - 3213

127

63.2

63.2

63.7

3214 - 6270

48

23.9

23.9

87.6

6271 - 9327

12

6.0

6.0

93.5

9328 - 12385

8

4.0

4.0

97.5

12386 - 15442

3

1.5

1.5

99.0

15443 - 18499

1

.5

.5

99.5

18500+

1

.5

.5

100.0

Total

201

100.0

100.0

Statistics

profit before income tax (Binned)

N

Valid

201

Missing

0

Mean

2.59

Median

2.00

Mode

2

Std. Deviation

1.022

The above shows the descriptive statistics for the variable “profit before income tax”, it can be seen that the mean value for this variable is 2.56 and the standard deviation is 1.02 which shows that data is not too much scattered.

From above histogram it can be found out that the distribution has been distributed positively.

a. Is there evidence of different profit across the different type of industry?

Ho: there is an evidence of different profit across the different type of industry

H1: there is not evidence of different profit across the different type of industry

level of significance: 0.05

We need to perform one way ANOVA:

ANOVA

profit before income tax

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

7.803E8

4

1.951E8

30.416

.000

Within Groups

1.257E9

196

6413272.878

Total

2.037E9

200

From the above table It can be seen that the p value is les than level of significance which lead us to conclude that there is an evidence of different profit across the different type of industry

b. Excluding the mining industry and the manufacturing industry, is there evidence of different profit among the retail trade industry, information media and telecommunication industry, and rental, hiring and real estate industry?

ANOVA

selctedind

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

1.209E7

2

6047066.670

10.820

.000

Within Groups

6.036E7

108

558858.075

Total

7.245E7

110

From the above table It can be seen that the p value is les than level of significance which lead us to conclude that there is an evidence of different profit across the different type of industry

Q: Between the mining industry and manufacturing industry, is there any evidence to suggest that the mining industry has higher profit before income tax than the manufacturing industry?

Ho: the mining industry has higher profit before income tax than the manufacturing industry

H1: the mining industry has not higher profit before income tax than the manufacturing industry

Group Statistics

VAR00007

N

Mean

Std. Deviation

Std. Error Mean

VAR00006

Mining industry

53

5.9566E3

4668.97544

641.33310

Manufacturing industry

37

4.8920E3

1323.71075

217.61671

Independent Samples Test

Levene's Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

VAR00006

Equal variances ...