change the words in the sentences but keep the main idea. don’t change anything in the boxes add references

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2.

Correlations

Months with service

Household income in thousands

Months with service

Pearson Correlation

1

.243**

Sig. (2-tailed)

.000

N

1000

1000

Household income in thousands

Pearson Correlation

.243**

1

Sig. (2-tailed)

.000

N

1000

1000

**. Correlation is significant at the 0.01 level (2-tailed).

3.

Correlation coefficient between “Months with service (tenure)” and “Household Income (income) is 0.243 as obtained from SPSS. This value is significant at 1% level of significance. This implies that there is weak positive linear relationship between the two variables. That is as the value of Months with service increases the value of household income increases slightly.

4.

Correlation doesn’t necessarily mean causation. Correlation measures the degree of association between the two variables. Or in other words it measures the strength of linear relationship between them. But causation means the change in one variable is caused by other.

5.

The value of correlation coefficient is 0.243. There is weak positive linear relationship between the two variables. That is as the value of Months with service increases the value of household income increases slightly.

6.

Correlations

Level of education

Age in years

Kendall’s tau_b

Level of education

Correlation Coefficient

1.000

-.112**

Sig. (1-tailed)

.

.000

N

1000

1000

Age in years

Correlation Coefficient

-.112**

1.000

Sig. (1-tailed)

.000

.

N

1000

1000

Spearman’s rho

Level of education

Correlation Coefficient

1.000

-.152**

Sig. (1-tailed)

.

.000

N

1000

1000

Age in years

Correlation Coefficient

-.152**

1.000

Sig. (1-tailed)

.000

.

N

1000

1000

**. Correlation is significant at the 0.01 level (1-tailed).

7.

According to Kendall’s tau b, the value of correlation coefficient is -0.112. This implies that there is weak negative relationship or almost no relationship between the two variables.

According to spearman’s tho the value of correlation coefficient is -0.152. This also implies that there is weak negative relationship or almost no relationship between the two variables.

8.

I used one-tailed test to test the significance of negative relationship between the two variables namely “Level of Education” and “Age in Years.” Here my null hypothesis is that the value of correlation coefficient is not significant, that is p =0. While my alternative hypothesis is that the value of correlation coefficient is significant, that is p < 0.

9.

Pearson product-moment correlation coefficient is calculated between two variables which are measured on interval or Ratio scale of measurement. The Ratio level of measurement has equal differences between scale values and equal quantitative meaning. It has a true zero point. A true zero point means that a value of zero on the scale represents zero quantity of the construct being assessed. Here, both tenure andincome variables are measured on Ratio scale of measurement.

Spearman’s and Kendall’s Correlation coefficient is calculated between two variables which are measured on a ordinal scale. The ordinal level of measurement describes variables that can be ordered or ranked in some order of importance. Here age and education are measured on ordinal level of measurement. It measures the monotonic relationship between the two variables.


10.

Marital status * Churn within last month Crosstabulation

Count

Churn within last month

Total

No

Yes

Marital status

Unmarried

358

147

505

Married

368

127

495

Total

726

274

1000

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Exact Sig. (2-sided)

Exact Sig. (1-sided)

Pearson Chi-Square

1.498a

1

.221

Continuity Correctionb

1.329

1

.249

Likelihood Ratio

1.499

1

.221

Fisher’s Exact Test

.229

.124

N of Valid Cases

1000

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 135.63.

b. Computed only for a 2×2 table

Null hypothesis Ho: Marital status and Churn within last month is independent. Versus alternative hypothesis, H1: Marital status and Churn within last month isn’t independent. With p > 0.05, I fail to reject Ho at 5% level of significance and conclude that marital status and Churn within last month are independent.

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