**1. What Is One Sample T-test?**

**Answer: **T-test is any statistical hypothesis test in which the test statistic follows a Student’s t distribution, if the null hypothesis is supported.

**[h,p,ci] = ttest(y2,0)% return 1 0.0018 ci =2.6280 7.0863**

**2. What Is Alternative Hypothesis?**

**Answer: **It is denoted by H1, is the statement that must be true if the null hypothesis is false.

**3. What Is Significance Level?**

**Answer: **The probability of rejecting the null hypothesis is called the significance level α , and very common choices are α = 0.05 and α = 0.01.

**4. What Is Binomial Probability Formula?**

**Answer: **P(x)= p x q n-x n!/[(n-x)!x!]

**–** Where n = number of trials.

**–** x = number of successes among n trials.

**–** p = probability of successess in any one trial.

**–** q = 1 -p.

**5. Explain Hash Table?**

**Answer: **It is a data structure used to implement an associative array, a structure that can map keys to values. To compute an index into an array of buckets or slots, it uses a hash function from which the correct value can be found.

**6. What are the differences between overfitting and underfitting?**

**Answer:** In statistics and machine learning, one of the most common tasks is to fit a *model* to a set of training data, so as to be able to make reliable predictions on general untrained data.

** Overfitting:** It is a statistical model describes random error or noise instead of the underlying relationship. It occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. Where it is a model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.

* Underfitting: It* is a statistical model or machine learning algorithm cannot capture the underlying trend of the data. It would occur; For example, when fitting a linear model to non-linear data.Such a model too would have poor predictive performance.

**7. Differentiate between univariate, bivariate and multivariate analysis.**

*Answer: Univariate** analyses* are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis.

*The **bivariate** analysis* attempts to understand the difference between two variables at a time as in a scatterplot. For Instance, analyzing the volume of sale and spending can be considered as an example of bivariate analysis.

* Multivariate analysis* deals with the study of more than two variables to understand the effect of variables on the responses.