STATISTICS

DEFINITION

TYPES

STATISTICS FORMULA'S

GLOSSARY TERMS

Alternative Hypothesis - is the hypothesis used in hypothesis testing that is contrary to the null hypothesis.

Continuous Variable- can take on an unlimited number of values between the lowest and highest points of measurement.

Deviation is a measure of difference between the observed value of a variable and some other value, often that variable's mean.

Discrete Variable is one that cannot take on all values within the limits of the variable.discrete variable is one that cannot take on all values within the limits of the variable.

Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true

Margin of Error - tells you how many percentage points your results will differ from the real population value.

Mean or average that is used to derive the central tendency of the data in question.

Median is the value separating the higher half from the lower half of a data sample

Parameters

Statistics is a form of mathematical analysis that uses quantified models, representations and synopses for a given set of experimental data or real-life studies. Statistics studies methodologies to gather, review, analyze and draw conclusions from data.

Importance of Statistics

  1. Statistics provides us different types of organized data with the help of graphs, diagrams and charts.
  1. The statistical methods helps us to research on different streams such as medicine, economics, business, social science and so on.
  1. Statistics comes handy while we do critical analysis.

Descriptive Statistics, which summarize data from a sample using indexes such as the mean or standard deviation.

Inferential Statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).

Population refers to the total set of observations that can be made.

Sample is a collection of units from a population.

Statistics these formulas assume simple random sampling.

Sample mean
x = ( Σ xi ) / n

Population Mean


Population mean = μ = ΣX / N


Sample mean = x = Σx / n

Population standard deviation


σ = sqrt [ σ2 ] = sqrt [ Σ ( Xi - μ )2 / N ]

Population variance
σ2 = Σ ( Xi - μ )2 / N

Standardized score
Z = (X - μ) / σ

Sample standard deviation


sqrt [ Σ ( xi - x )2 / ( n - 1 ) ]

Sample variance
s2 = Σ ( xi - x )2 / ( n - 1 )