What are variables, Different type of variables, Classify variables into qualitative, quantitative,
discrete and continuous variables
Define Dependent and Independent variables
Breakdown the range of a series of quantitative measurements into intervals and specify which
measurement belongs to which intervals.
3.
Define the data types and the scales of measurements
Continuous and discrete data sets
Ordinal and nominal data sets
Interval scales
Composite scales
4.
Interpret a given data: Apply descriptive statistics for continuous variables in terms of:
Measures of central tendency: Calculate the mean, median and mode and interpret them.
Measures of dispersion: variance, standard deviation, coefficient of variation
Measures of shapes: regarding the distribution of the data sets
5.
Apply frequency distribution to a given data and its interpretation. What are Percentiles, their uses
and limitations in a dataset
6.
Apply the concepts of probability.
7.
Define Probability, types of probability with examples.
8.
Describe the common probability distributions especially Normal and Binomial distributions.
List the descriptive properties of a normal distribution with mean µ and standard deviation s
Use tables of normal distribution function to estimate the area under a normal curve with mean
µ and s for one and between 2 values of the variable.
Define Binomial distribution: use the normal approximation to the binomial probabilities and use
of continuity correction to improve the estimates.
9.
Describe Population and its relation to sample. Recognize the algebraic notations used in statistics to
differentiate between parameters and statistics.
10.
Define Sampling and its techniques:
Distinguish between the probability and non-probability sampling
Define various types of probability and non-probability sampling
Why sampling errors arise in a sample estimate of a parameter.
Describe the sampling distributions of a mean and a proportion.
Interpret and explain quantitatively the effect of the standard deviation and sample size on the
sampling distributions
11.
Calculate the sampling errors; calculate the standard error of a mean and a proportion and its
interpretation.
12.
Calculate and interpret confidence intervals for a parameter. Explain why it is necessary to calculate
confidence interval in a data
13.
Apply concepts of Comparing data (Inferential statistics):
Learn about the basics of hypothesis development
What is a Null hypothesis and Alternate Hypothesis
