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.
Introduction to Business Statistics
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
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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
Addresses hypothesis testing methodology
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
Address measuring autocorrelation
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
Discusses adjusted r2
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
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