# ANALISIS REGRESI opening regression. Deskripsi matakuliah Mempelajari :  Analisis regresi linear sederhana  Analisis regresi linear berganda  Asumsi-asumsi.

## Presentation on theme: "ANALISIS REGRESI opening regression. Deskripsi matakuliah Mempelajari :  Analisis regresi linear sederhana  Analisis regresi linear berganda  Asumsi-asumsi."— Presentation transcript:

ANALISIS REGRESI opening regression

Deskripsi matakuliah Mempelajari :  Analisis regresi linear sederhana  Analisis regresi linear berganda  Asumsi-asumsi dalam regresi  Estimasi koefisien dan persamaan regresi  Inferensi dan interpretasi dalam regresi  Analisis variansi pada regresi  Pendekatan matriks dalam analisis regresi  Jumlah kuadrat ekstra  Analisis korelasi  Regresi lain (regresi polinomial, regresi dummy,regresi logistik, regresi PLS) opening regression

Referensi Neter, John. 1990. Applied Linear Statistical Models : Regression, Analysis of Variance, and Experimental Design. Irwin : Boston Model linier terapan I dan II (terjemahan) Sumantri, B. (1997). Model Linear Terapan, Buku I. Jurusan Statistika: FMIPA IPB Sumantri, B. (1997). Model Linear Terapan, Buku II. Jurusan Statistika: FMIPA IPB Myers, R.H. (1996). Classical and Modern Regression with Applications. Boston : PWS-KENT Publishing Company Sembiring. (1995). Analisis Regresi, Bandung : ITB opening regression

Kontrak perkuliahan opening regression

Why study statistics? Make decision without complete informations Understanding population, sample Parameter, statistic Descriptive and inferential statistics opening regression Intro…

glossary A population is the collection of all items of interest or under investigation N represents the population size A sample is an observed subset of the population n represents the sample size A parameter is a specific characteristic of a population Mean, Variance, Standard Deviation, Proportion, etc. A statistic is a specific characteristic of a sample Mean, Variance, Standard Deviation, Proportion, etc. opening regression

Population vs. Sample opening regression a b c d ef gh i jk l m n o p q rs t u v w x y z PopulationSample b c g i n o r u y Values calculated using population data are called parameters Values computed from sample data are called statistics

Examples of Populations Incomes of all families living in yogyakarta All women with pregnancy problem. Grade point averages of all the students in your university … opening regression

Random sampling Simple random sampling is a procedure in which each member of the population is chosen strictly by chance, each member of the population is equally likely to be chosen, and every possible sample of n objects is equally likely to be chosen The resulting sample is called a random sample opening regression

Descriptive and Inferential Statistics Two branches of statistics: Descriptive statistics Collecting, summarizing, and processing data to transform data into information Inferential statistics Provide the bases for predictions, forecasts, and estimates that are used to transform information into knowledge and decision opening regression

Descriptive Statistics Collect data e.g., Survey Present data e.g., Tables and graphs Summarize data e.g., Sample mean = opening regression

Inferential Statistics Estimation e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing e.g., Test the claim that the population mean weight is 120 pounds opening regression Inference is the process of drawing conclusions or making decisions about a population based on sample results

The Decision Making Process opening regression Begin Here: Identify the Problem Data Information Knowledge Decision Descriptive Statistics, Probability, Computers Experience, Theory, Literature, Inferential Statistics, Computers

Independent and Dependent Variable Example case: A real estate agent wishes to examine the relationship between the selling price of a house (\$1000s) and its size(measured in square feets) Dependent variable (Y) = house price in \$1000s Independent variable (X) = house’size Dependent variable : response variable Independent variable : predictor variable opening regression

Sample Data for House Price Model House Price in \$1000s (Y) Square feets (X) 2451400 3121600 2791700 3081875 1991100 2191550 4052350 3242450 3191425 2551700 opening regression

Scatter plot opening regression

Graphical Presentation House price model: scatter plot and regression line Slope = 0.10977 Intercept = 98.248 opening regression

Bagaimana mendapatkan persamaan garis regresi ? Next Bawa kalkulator setiap perkuliahan regresi opening regression

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