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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