I need help on STATISTICS QUESTIONS. Please help me on it. THANKYOU !

The Australian Bureau of Statistics undertook a survey called the Longitudinal Survey of Australian Children (LSAC).They collected a vast amount of information about children, their health and development, their family circumstances and background, their parents' lifestyle choices, etc.

However, in this case study we will look at j

ust a few questions from this survey, concentrating on the parents.Let's take two quantitative variables: Income and AgeWe expect to see whether there is some relationship between household income and the head of family's age.

For this, you are required to:

(a) Draw a scatter plot to get an idea on the relationship between age and income levels.

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(b) Calculate a correlation coefficient to give us an idea if there is some form of linear association between these two (quantitative) variables and its strength. Comment also on the goodness of fit from relevant statistics.

(c) Conduct a simple linear regression of income on age, write down the specification of the model and the equation for the line of best fit. Interpret the estimated parameters.

(d) Perform a hypothesis test to determine if age is a significant predictor of income.

(e) Distinguish between correlation analysis and simple linear regression and explain the use of each method.

2000.00- 0 00 O OO DO O O 1750.00- oo O O O O 1500.00- Income OOO 1250.00- O CDO 1000.00- CD O O o OO oo 750.00- O GOOD . @ 6000 @>0 0 00 oo Doo8 O o O 10 30 40 50 60 Age Correlations Age Income Age Pearson Correlation 1 127* Sig. (2-tailed) .000 N 2500 2500 Income Pearson Correlation 127** Sig. (2-tailed) 000 N 500 2500 **. Correlation is significant at the 0.01 level (2-tailed).Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate 127a 016 016 336.08549 a. Predictors: (Constant), Age Variables Entered/Removeda Variables Variables Model Entered Removed Method Ageb Enter a. Dependent Variable: Income b. All requested variables entered. ANOVAa Model Sum of Squares df Mean Square F sig. 1 Regression 4608817.183 4608817.183 40.803 000b Residual 282157726.206 2498 112953.453 Total 286766543.388 2499 a. Dependent Variable: Income b. Predictors: (Constant), Age Coefficientsa Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta Sig. (Constant) 998.658 44.937 22.223 .000 Age 8.602 1.347 127 6.388 000 a. Dependent Variable: Income