Explain what chi squared goodness of fit is.

Goodness of fit:
Goodness of fit test is applied to check how well the sample data obtained fits the distribution of the selected population. It can also be viewed as whether the frequency distribution fits the given pattern. Most commonly used test to check the goodness of fit is the chi-square test.
There are two values involved are observed and the expected values. The observed value represents the frequency of particular category in the sample and the expected value is obtained from the given distribution. Moreover, it summarizes the difference between the expected and observed values of the given data.
The assumptions for the chi-square test are:
1. The sampling method should be simple random sampling (normality).
2. The variables should be categorical in nature.
3. The expected value of the number of sample observations in each level of the variable is at least 5.
The hypotheses are stated as given below:
Null hypothesis: Data comes from the specified distribution.
Alternative hypothesis: Data does not come from the specified distribution.
The chi-square test statistic is calculated using the formula given below:
${\displaystyle {\chi }^2=\sum \frac{{(O_i-E_i)}^2}{E_i}}$
Where,
${\displaystyle O_i}$ - Represents the observed values
${\displaystyle E_i}$ - Represents the expected values
The asymptotic distribution is chi-square.
Expected values:
Expected values are the product of the column and row total divided by the table total.
The expected frequency for the first cell is as shown below:
${\displaystyle E_{11}=\frac{\text{First total}\times \text{First column total}}{\text{Grand total}}}$
Similarly, calculations made for other cells also.
Now, with the expected values and obtained values the chi square test statistic is obtained.
Computation of P-value:
The P-value for the chi-square test at (n-1) degrees of freedom can be obtained using the excel formula, “=CHI.DIST.RT(test statistic, df)”.
Decision rule:
If ${\displaystyle \text{p-value}\le \alpha }$, then reject the null hypothesis.
Otherwise, do not reject the null hypothesis.
The conclusion is made based on the decision rule.