Descriptive statistics and ANOVA analysis are statistical methods. Descriptive statistics summarize data in a clear way and lay the foundation for statistical knowledge by analyzing the distribution, dispersion, and other characteristics of data. ANOVA (analysis of variance) is a technique used to analyze the effects of independent variables on a dependent variable. It extends the t-test to compare more than two sample means and determines whether the means are equal or different.

1. Descriptive statistics and anova analysis

2. Descriptive statistics:
 Summarization of a collection of data in a clear and
understandable way.
 the most basic form of statistics.
 lays the foundation for all statistical knowledge.

3. univariate analysis
The distribution
The dispersion
 bivariate and multivariate analysis
Use in statistics:

4. The distribution:
 Mean
 Median
 Mode
Arithmetic mean or mean:
Arithmetic mean or simply the
mean of a variable is defined as the sum of the observations
divided by the number of observations.
If the variable x assumes n values
x1, x2 … xn then the mean, X , is given by
X=∑x/n

5. Median:
The median is the middle most item that
divides the group into two equal parts,
one part comprising all values greater, and the other, all
values less than median.
When n is odd Median = Md = (n+1/2)th value.
When n is even Average of =(n/2)th and (n/2+1)th value.
(n is the number of observations)

6. Mode:
The mode refers to that value in a distribution,
which occur most frequently.

7. The dispersion:

8. Anova analysis

9. What is an ANOVA?
 One-way ANOVA stands for Analysis of
Variance
 Purpose:
 Extends the test for mean difference between
two independent samples to multiple
samples.
 Employed to analyze the effects of
manipulations (independent variables) on a
random variable (dependent).

10. Dependent variable:
the variable we are trying to explain, also
known as response variable (Y).
Independent variable:
also known as explanatory variables (X).
definitions:

11. H0 1= 2 = 3 …..= n
H1 1 2  3 …..  n
The null hypothesis tests whether the mean of all the
independent samples is equal
The alternative hypothesis specifies that all the
means are not equal
What does anova tests?

12. Thank you