From Past Exam

Question 1

An independent variable

Answer 1

An independent variable is the variable that is changed or controlled in a scientific experiment

The independent variable is what you change and an dependant variable changes because of that
Age and time are key ones

Question 2

A dependant variable

Answer 2

A dependant variable is a variable being tested and measured in an scientific experiment

An independent variable is what you chAnge, and the dependent variable is what changes because of that

Question 3

Interpreting regression analysis

Answer 3

R square figure shows the proportion of variance in the dependant variable which can be predicted from the independent variable. Eg .489 means that 48.9% of the variance can be predicted from the independent variables.

Adjusted r square attempts to yield a more honest value to estimate the r squared for the population

The f and sig, the p value associated if smaller than 0.05, can assume the indeendmt variable reliably predict the dependant variable.
If it’s over 0.05, you would say the group of independent variaiabkes does not reliably predict the dependant variable.

T and sig - if you use a 2 tailed test then you would compare each p value to your preselected value of alpha. Coefficients having p values less than alpha are statistically significant( I.e you can reject null hypothesis and say that the coefficient is significantly different from 0).
If you use a 1 tailed test, you divide the p value by 2 before comparing.

Question 4

Interpreting t test

Answer 4

First need to understand whether you pick assumed or not( this is the first column has 2 things you have to choose one of them).

You do this by comparing the significance value to the level of significance. If higher you accept the null hypothesis and accept the the first column. It also makes sense as the snagdard deviation, will be in the group statistics box is very similir too.

Then you compare the significance value in independent samples test box to the significance value set (0.05). If greater than 0.05, accept null hypothesis which says the mean scored between the groups is not significantly different.

Then, mean difference which is calculated by taking first mean (in group stat) and taking away from the one below it.

Lastly confidence interval looks at mean difference, so % difference that the actual difference competency is between them numbers.

Question 5

Understanding correlations

Answer 5

General interpretation: between -1 and 1. Close to -1 and 1 represents a very strong correlation.
0.7 = strong 
0.5 = moderate 
0.3 = weak
0= no correlation 

If strong correlation then it relates etc “this means that’s companies with more employees, higher market capitalisation and a good reputation generally have a higher stock price.

Question 6

Regression analysis example

Answer 6

R square is 0.5% of the variance in stock price of bmw is explained by the online presence of the company. This means that there are many more variables that influence BMWs stock price and online presence doesn’t explain a lot.

In coefficients table you check significance of the variable. When it is significant you can predict the direction and size of the effect.
The dog is .297>0.05 which means that the online presence of bmw does not predict its stock price. If it would have been significant we could have interpreted the unstandardised coefficient b. In this case it would have meant if the online presence increased by 1, the stock price would have decereadd by 0.36. But it is not significant.

Question 7

Elasticity

Answer 7

NUmber is less than 1, demand is inelastix. Doesn’t respond much to price change.
If =1, elasticity Of demand is unitary.
If number is greater than 1, the demand is elastic. Demand changes a lot when the price changes.

Calc elasticity:
(Q1-Q2) / (Q1+Q2) all divides by
(P1-P2) / (P1+ P2)