# Monthly Archives: May 2020

## Exercise Sheet 5

1d theke clear na , eta clear korte hobe , In Sha Allah.

Lagle onno kono tutorial ba example dekhte hobe.

## IDA Old Question Solve

Winter 2019/20:

Normal distribution: meu=152
sigma=4.0

The Normal Distribution has:
Mean=Median=Mode
50% of value less than the mean and 50% greater than the mean

1(b) Weibull

1(c)
https://www.statisticshowto.com/triangular-distribution/

(but I need to clear it up about Professor Orth solution)

2a

chi squaRE TEST

DEGREES of freedom

STATISTICS what it is?

Winter 2018/19:

## Exercise Sheet 4

Data Mining Methods: Unit 4
Correlation and Simple Linear Regression

Interpretation of the correlation coefficient
Possible range: [-1, 1]
-1: perfect negative linear relationship
0: no linear relationship,
1: perfect positive linear relationship.

Regression: Objective

To predict one variable from other variables.
To explain the variability of one variable using the other variables.

Predicts scores on one variable from the scores on a second variable.

Response variable: predicting variable (Y )
Predictor variable: predictions based on this variable (X)

Simple regression:
Only one predictor variable; otherwise multiple regression

Linear regression:

Predictions of the response variable (Y ) is a linear function of  the predictor variable (X)

## Data Preprocessing/Exercise Sheet 2

Theory:
Data Preprocessing in the Data Mining Process:

The data mining/KDD process
Why data preprocessing?

Issues in Data Preprocessing:

Data Cleaning
Data Transformation
Variable Construction
Data Reduction and Discretization
Data Integration

The data mining/KDD Process:
Understanding customer: 10%-20%
Understanding data:20-30
Prepare data: 40-70%
Build Models: 10-20%
Evaluate models: 10%-20%
Take action:10%20%

Why data mining?

Real – world data is dirty
Low data quality anyway a huge problem in data mining
Garbage in,garbage out
Different methods, different requirements

R Working Codes for data mining:

R code is case sensitive:
I am doing it from professors sheet.

dim means dimension

This line i could not make work:

hist(Ozone,breaks=25,ylim=(c(0,45)),main=”Original data”)

And another question how the imputation works

Exercise 2 (K)= I have to find the answers