## STATISTIK BISNIS

STATISTIK-BISNIS adalah dua hal yang tidak dapat dipisahkan. Dalam Bisnis apapun bidang yang digeluti tentu memerlukan berbagai perhitungan dan analisa dan akhirnya diperlukan untuk mengambil keputusan yang tepat dalam rangka peningkatan usaha ataupun untuk menyelesaikan berbagai permasalahan.

STATISTIK adalah bidang ilmu yang berkaitan dengan data, yaitu pengumpulan data, pemrosesan dan pengolahan data,penyajian data, dseskripsi data, dan interferensi hasil pengolahan data dan keputusan atas hasil pengolahan data.

Sehingga antara sttistik dan bisnis selalu berpasangan dan berkaitan erat.

1. How decisions are often based on incomplete information

2. Explain key definition : – Population VS Sample, – Parameter VS Statistics, – Descriptive VS Inferential Statistics

3. Describe random sampling

4. Explain the difference between Descriptive and Inferential Statistics

DATA PRESENTATION

Developing tables and charts for categorical data Developing tables and charts for numerical data The principles of properly presenting graphs Examination of cross tabulated data using the contingency table and side-by-side bar chart

 Identify types of data and levels of measurement

 Create and interpret graphs to describe categorical variables :

 Create a line chart to describe time-series data

 Create and interpret graphs to describe numerical variables :

 Construct and interpret graphs to describe relationships between variables

 Describe appropriate and inappropriate ways to display data graphically

Modul Praktikum Bisnis Statistika

NUMERICAL DESCRIPTIVE MEASURES

Measures of central tendency, variation, and shape Population summary measures Five number summary and Box-and-Whisker plots Covariance and Coefficient of correlation

 To describe the properties of central tendency, variation and shape in numerical data

 To calculate descriptive summary measures for a population

 To construct and interpret a Box-and-Whisker plot

 Explain the weighted mean and when to use it

 To describe the covariance and coefficient of correlation

BASIC PROBABILITY

• Discussion on basic probability concept

• Sample spaces and events, contingency tables, simple probability and joint probability

• Discuss Bayers’ theorem

• Examine basic probability rules

• Define conditional probability

• Statistical independence, marginal probability, decision trees, and the multiplication rule

DISCRETE PROBABILITY

Addressed the probability of a discrete random variable Define covariance and discuss its application in finance To compute probability from the binomial, Poisson and Hypergeometric distribution How to use this distribution to solve business problem

 The properties of probability distribution

 The expected value, variance and covariance of a probability distribution

 The probability of a discrete random variable

 Binomial Distribution

 Poisson Distribution

 Hypergeometric distribution

Normal & Sampling Distribution

NORMAL AND SAMPLING DISTRIBUTION

Define continuous distribution: normal, uniform and exponential Probabilities using formulas and tables The concept of the sampling distribution The importance of the Central Limit Theorem Examine when to apply different distributions

 Recognize when to apply the normal, uniform and exponential probability distribution

 Explain jointly distributed variables and linear combinations of random variables

 Describe a simple random sample and why sampling is important

 Explain the difference between descriptive and inferential statistics

 Define the concept of a sampling distribution

 Determine the mean and standard deviation for the sampling distribution of the sample mean and sample proportion

 Describe the Central Limit Theorem and its importance

 Applied distribution to decision problems

Sampling

 To distinguish between different survey sampling methods

 The concept of the sampling distribution

 To compute probabilities related to the sample mean and the sample proportion

 The importance of the Central Limit Theorem

CONFIDENCE INTERVAL

 Discuss the concept of confidence intervals

 Distinguish between a point estimate and a confidence interval estimate

 Construct and interpret a confidence interval estimate for a single population mean using both the Z and t distribution

 Form and interpret a confidence interval estimate for a single population proportion

 Created confidence interval estimates for the mean which is σ known and unknown

 Created confidence interval estimates for the proportion

HYPOTHESIS TESTING

The basic principles of hypothesis testing How to use hypothesis testing to test a mean or proportion The assumption of each hypothesis-testing procedure, how to evaluate them and the consequences if they are violated Formulate a decision rule for testing a hypothesis Know Type I and Type II errors

 Formulate null and alternative hypothesis

 Performed Z test for the mean σ known

 Discuss critical value and p-value approaches to hypothesis testing

 Performed one-tail dan two-tail test

 Performed t test for the mean σ unknown

 Performed Z-test for the proportion

 Asses the power of a test

TWO SAMPLE TEST

Use hypothesis testing for comparing the difference between: The means of two independent populations The means of two related populations The proportions of two independent populations The variances of two independent populations

 Compared two independent samples

 Compared two related samples

 Compared two population proportions

 Performed F test for the difference between two population variances

 Use F table to find F critical values

Analysis of Variances

The basic concepts of experimental design How to use the one-way analysis of variance to test for the differences among the means of several groups How to use the two-way analysis of variance and interpret the interaction

 Described one-way analysis of variance

 The logic of ANOVA

 ANOVA assumptions

 F test for the difference in c means

 The Tukey-Kramer procedure for multiple comparasons

 Described Two-way analysis of variance

CHI SQUARE AND NON PARAMETRIC TESTS

How and when to use the chi-square test for contingency tables How to use the Marascuillo procedure for determining pair-wise differences when evaluating more than two porportions How and when to use the McNemar test How and when to use nonparametric tests

 Developed and applied the χ2 test for the difference between two proportions

 Developed and applied the χ2 test for the difference between two proportions

 Examine the χ2 test for the independence

 Used the McNemar test for differences in two related proportions

 Used the Wilcoxon rank sum test for two population medians

 Appiled the Kruskal-Walls H-test for multiple population medians

SIMPLE LINEAR REGRESSION

Using regression analysis to predict the value of a dependent variable based on an independent variable The meaning of the regression coefficients b0 and b1 Evaluating the assumptions of regression analysis and know what to do if the assumptions are violated Making inferences about the slope and correlation coefficient Estimating mean values and predict individual values

 Explain the simple linear regression model

 Determine the simple linear regression equation

 Use a regression equation for prediction

 Describe measures of variation

 Discuss residual analysis

 Describe inference about the slope

 Address estimation of mean values and prediction of individual values

MULTIPLE REGRESSION

How to develop a multiple regression model How to interpret the regression coefficients How to determine which independent variables are most important in predicting a dependent variable How to use quadratic terms in a regression model How to measure the correlation among independent variables

 Developed the multiple regression model

 Tested the significance of the multiple regression model

 Discussed using residual plots to check model assumptions

 Developed the quadratic regression model

 Described collinearity

 Described model building

TIME SERIES FORECASTING

Discussed the important of forecasting Performed smoothing of data series Described least square trend fitting and forecasting Addressed time series forecasting Addressed autoregressive models Described procedure for choosing appropriate models

 Time series forecasting models

 Moving average and exponential smoothing

 Linear, exponential and quadratic trend

 The autoregressive and the least-square models for seasonal data

 Prices indexes

 Aggregated and simple indexes