Theory revision 2

• population
• it is a collection of possible individuals, objects or measurement of interest
• example: population of Australia.
• sample
• it is a portion or part of the population of interest
• example: 30 of UTS students
• parameter
• it is a measurable characteristic of a population
• example: total population of Melbourne
• statistic
• it is a measurable characteristic of a sample.
• example: 15 of wine lovers
• statistical inference
• it is the process of drawing conclusion from data subject to random variation such as observational errors and sample variation.
• example: 15 percent of beer lovers

Theory revision 1

• Statistic is the science of collecting, organizing, presenting, analyzing and interpreting numerical data to assist in making more effective decision. This technique is used extensively by marketing, accounting, quality control, consumers, professional sports people, hospital administrators , educators, politician and so on,
• Descriptive statistics is the methods of organizing, summarizing and presenting data in an informative way.
• Inferential statistics is a decision, estimate, prediction or generalization about a population, based on sample.
• Qualitative data is the characteristic or variable being studied is non numeric.
• Quantitative data is the variable that can be reported numerically.
• A population is a collection of possible individuals, objects or measurement of interest.
• A sample is a portion or part of the population of the interest.

Homoscedasticity

In statistics, a sequence or a vector of random variables is homoscedastic, if all random variables in the sequence or vector have the same finite variance.

Theory revision 5

The multiple standard error of estimate is a measure of the effectiveness of the regression equation.

• It is measured in the same units as the dependent variable.
• It is difficult to determine what is a large value and what is a small value of the standard error.
• The independent variables and the dependent variable have a linear relationship.
• The dependent variable must be continuous and at least interval scale.
• The variation in (Y-Y') or residual must be the same for all values of Y. When this is the case, we say the difference exhibits homoscedaticity.
• A residual is the difference between the actual value of Y and the predicted value Y'.
• Residuals should be approximately normally distributed, Histograms and stem and leaf charts are useful in checking this requirement.
• The residuals should be normally distributed with mean 0.
• Successive values of the dependent variable must be uncorrelated.

The ANOVA table 

• The ANOVA table gives the variation in the dependent variable(of both that which is and is not explained by the regression equation),
  
• It is used as a statistical technique or test in detecting the differences in population means or whether or not the means of different groups are all equal when you have more than two population.

Correlation Matrix 

• A correlation matrix is used to show all possible simple correlation coefficients between all variables.
• the matrix is useful for locating correlated independent variables.
• How strongly each independent variable is correlated to the dependent variable is shown in the matrix.

Global Test 

• The global test is used to investigate whether any of the independent variables have significant coefficients. 
• The test statistic is the F distribution with k (number of independent variables) and n-(k+1) degree of freedom, where n is the sample size.

Test for individual variables

• This test is used to determine which independent variables have non zero regression coefficient.
• The variables that have zero regression coefficients are usually dropped from the analysis.
• The test statistic is the t distribution with n-(k+1) degrees of freedom.

Qualitative Variables and Stepwise Regression

• Qualitative variables are non numeric and also called dummy variables
• For a qualitative variable, there are only two conditions possible.
• Stepwise Regression leads to the most efficient regression equation
• Only independent variables with significant regression coefficients are entered into the analysis. Variables are entered in the order in which they increase R^2 the fastest.

Theory revision 4

Hypothesis Testing

• Testing hypothesis is an essential part of statistical inference.
• A statistical hypothesis is an assumption about a population. This assumption may or may not be true.
• For example, claiming that a new drug is better than the current drug for treatment of the same symptons.
• The best way to determine whether a statistical hypothesis is true would be to examine the entire population.
• Since that is often impractical, researchers examine a random sample from the population.If sample data are consistent with the statistical hypothesis, the hypothesis is accepted if not, it is rejected.
• Null Hypothesis: the null hypothesis denoted by H0 is usually the hypothesis that sample observations result purely from chance.
• Alternative Hypothesis: The alternative hypothesis denoted by H1 is the hypothesis that sample observations are influenced by some non random cause.
• Statisticians follow a formal process to determine whether to accept or reject a null hypothesis based on sample data. This process, called hypothesis testing consists of five steps.
1. State null and alternative hypothesis
2. write down relevant data, select a level of significance
3. Identify and compute the test statistic, Z to be used in testing the hypothesis.
4. Compute the critical values Zc.
5. Based on the sample arrive a decision.
• Decision errors
• Type I error: A type I error occurs when the null hypothesis is rejected when it is true. Type I error is called the significance level. This probability is also denoted by Alpha. α
•  Type II error: A type II error occurs when the researcher accepts a null hypothesis that is false.
•  One-tailed and two-tailed tests
• A test of a statistical hypothesis, where the region of rejection is on only one side of the sampling distribution is called a one tailed test.
• For example, suppose the null hypothesis states that the mean is equal to or more than 10. The alternative hypothesis would be that the mean is less than 10.
• The region of rejection would consist of a range of numbers located on the left side of sampling distribution that is a set of numbers less than 10.
• A test of a statistical hypothesis where the region of rejection is on both sides of the sampling distribution is called a two tailed test.
• For example, suppose the null hypothesis would be that the mean is equal to 10, the alternative hypothesis would be that the mean is less than 10 or greater than 10.
• The region of rejection would consist of a range of numbers located on both sides of sampling distribution; that is the region of rejection would consist partly of numbers that were less than 10 and partly numbers that were greater than 10.
• Degree of freedom
• The concept of degree of freedom is central to the principle of estimating  Statistics of population from samples of them.
• It is the number of scores that are free to try.

Theory revision 3

Bernuolli Trials 

• Random with two outcomes (success or failure)
• Random variable X often coded as 0(failure) and 1(success)
• Bernoulli trail has probability of success usually denoted p.
• Accordingly probability of failure (1-p) is ususally denoted
• q=1-p
• where x can be zero or one.
• probability of Bernoulli Distribution is;  
  

Binomial distribution

1. identical number of trials
2. the binomial distribution which consists of a fix number of statistically independent BErnoulli trials.
3. 2 possible outcome for each trials(success or failure)
4. each trial is independent(does not affect the others)
5. probability of success is the same for each trial
6. Shapes of binomial distribution
• if p<0.5: the distribution will exhibit positive skew
• if p=0.5: the distribution will be symmetirc
• if p>0.5: the distribution will exhibit negative skew

Poisson Random Variable 

• Poisson random variable represents the number of independent events that occur randomly over unit of times.
• Count number of times as event occur during a given unit of measurement.
• Number of events that occur in one unit is independent of other units.
• Probability that events occurs over given unit is identical for all units.(constant rate)
• Events occur randomly
• Expected number of events(rate) in each unit is denoted by λ(lambda)

Residual Plpt

Analysis of Residuals

• A residual is the difference between the actual value of Y and the predicted value Y'.
• Residual should be approximately normally distributed. Histograms and stem and leaf chars are useful in checking this requirement.
• A plot of the residuals and their corresponding Y' values is used for showing that there are no trends or patterns in the residuals.

Qualitative Variables & Stepwise Regression

• Qualitative variables are non-numeric and are also called dummy variables.
• For a qualitative variable, there are only two conditions possible
• Stepwise Regression leads to the most efficient regression equation.
• Only independent variables with significant regression coefficients are entered into the analysis. Variables are entered in the order in which they increase R^2 the fastetst.

Test for individual variables

• This test is used to determine which independent variables have non zero regression coefficients.
• The variables that have zero regression coefficients are usually dropped from the analysis.
• The test statistic is the t distribution with n-(k+1) degrees of freedom.

Global Test

• The global test is used to investigate whether any independent variables have significant coefficients. The hypotheses are:

The test statistic is the F distribution with k ( number of independent variables) and n(k+1) degrees of freedom, where n is the sample size.

Correlation Matrix

• A correlation matrix is used to show all possible simple correlation coefficients between all variables.
• The matrix is useful for locating correlated independent variables.
• How strongly each independent variable is correlated to the dependent variable is shown in the matrix.

The ANOVA table

• The ANOVA table gives the variation in the dependent variable ( of both that which is and is not explained by the regression equation).
• It is used as a statistical technique or test in detecting the differences in population means or whether or not the means of different groups are all equal when you have more than two populations.

Multiple Regression and Correlation

• The independent variables and the dependent variable have a linear relationship.
• The dependent variable must be continuous and at least interval-scale.
• The variation in (Y-Y') or residual must be the same for all values of Y. When this is the case, we say the difference exhibits homoscedasticity.
• The residuals should be normally distributed with mean 0.
• Successive values of the dependent variable must be uncorrelated.

Multiple Standard Error of Estimante

• The multiple standard error of estimate is a measure of the effectiveness of the regression equation.
• It is measured in the same units as the dependent cariable.
• It is difficult to determine what is a large value and what is a small value of the standard error.
• The formula is 

• where n is the number of observations and k is the number of independent variables.

Multiple Regression Analysis

• For two independent variables, the general form of the multiple regression equation is
• Y'=a+b1X1+b2X2
• X1 and X2 are the independent variables.
• a is the Y intercept
• b1 is the net change in Y for each unit change in X1
• holding X2 constant. It's called a partial regression coefficient a net regression coefficient or just a regression coefficient.
• The general multiple regression with k independent variable is givien by:
• The least squares criterion is used to develop this eauation.
• Calculating, b1, b2, etc is very tedious there are many computer software packages that can be used to estimate these parameters

Regression Analysis

• Purpose: to determine the regression equation; it is used to predict the value of the dependent variable (Y) based on the independent variable (X). 
• Procedure: select a sample from the population and list the paired data for each observation; draw a scatter diagram to give a visual portrayal of the relationship; determine the regression equation.
• The regression line 
• A straight line that represents the relationship between two variables 
• Useful to add to the scatterplot to help us see the direction of the relationship 
• But it’s much more than this… 
• Prediction 
• Regression line enables us to predict Variable Y on the basis of Variable X

• b 
• The slope of the regression line 
• The amount of change in Y associated with a one-unit change in X
•  a 
• The intercept 
• The point where the regression line crosses the Y axis 
• The predicted value of Y when X = 0

Rank Order Correlation

• The correlation coefficient(r) is based on the assumption that data is normally distributed.
• If the data are skewed, instead of r we use a rank order correlation rs (also called spearman coefficient) as follows.

• Where d=difference between the ranks of each pair
• N=the number of pair of observations
• The spearman coefficient will assume any value between -1.00 to + 1.00 inclusive.
• -1.00 indicates a perfect negative correlation
• +1.00 indicates a perfect positive correlation
• The ranking order has to be done for each pair of data
• The highest data value will be lowest rank or the other way.
• The lowest data value will be highest rank or the other way.

t value

• with (n-2) degree of freedom
• df= number of points on scatter plot minus 2

Formula for correlation coefficient (r)