Presentasi sedang didownload. Silahkan tunggu

Presentasi sedang didownload. Silahkan tunggu

REGRESI LINIER SEDERHANA KULIAH #2 ANALISIS REGRESI Usman Bustaman.

Presentasi serupa


Presentasi berjudul: "REGRESI LINIER SEDERHANA KULIAH #2 ANALISIS REGRESI Usman Bustaman."— Transcript presentasi:

1 REGRESI LINIER SEDERHANA KULIAH #2 ANALISIS REGRESI Usman Bustaman

2 APA ITU? Regresi Linier Sederhana

3 REGRESI (Buku 5: Kutner, Et All P. 5) Sir Francis Galton (latter part of the 19th century): -studied the relation between heights of parents and children -noted that the heights of children of both tall and short parents appeared to "revert" or "regress" to the mean of the group. -developed a mathematical description of this regression tendency, -  today's regression models (to describe statistical relations between variables).

4 LINIER Masih ingat Y=mX+B? Slope? Konstanta? B m X Y

5 LINIER LEBIH LANJUT… -Linier dalam paramater… -Persamaan Linier orde 1: -Persamaan Linier orde 2: -Dst… (orde  pangkat tertinggi yang terdapat pada variabel bebasnya)

6 SEDERHANA Relasi antar 2 variabel: 1 variabel bebas (independent variable) 1 variabel tak bebas (dependent variable) Y=mX+B? Mana variabel bebas? Mana variabel tak bebas? B m X Y

7 BAGAIMANA MEMBANGUN MODEL REGRESI LINIER SEDERHANA? Analisis/ Comment Grafik-2 Berikut:

8 Analisis/Comment Grafik-2 Berikut: A B CD

9 FUNGSI RATA-2 ( Mean Function ) If you know something about X, this knowledge helps you predict something about Y.

10 PREDIKSI TERBAIK…  Bagaimana mengestimasi parameter dengan cara terbaik…

11 Regresi Linier

12 Koefisien regresi Populasi Sampel ˆ Y = b 0 + b 1 X i

13 Regresi Linier  Model i  X Y YX    YiYi XiXi ? (the actual value of Y i )

14 REGRESI TERBAIK = MINIMISASI ERROR -Semua residual harus nol -Minimum Jumlah residual -Minimum jumlah absolut residual -Minimum versi Tshebysheff -Minimum jumlah kuadrat residual  OLS

15 ORDINARY LEAST SQUARE (OLS)

16 ASSUMPTIONS Linear regression assumes that… 1. The relationship between X and Y is linear 2. Y is distributed normally at each value of X 3. The variance of Y at every value of X is the same (homogeneity of variances) 4. The observations are independent

17 ASUMSI LEBIH LANJUT… Alexander Von Eye & Christof Schuster (1998) Regression Analysis for Social Sciences

18

19 PROSES ESTIMASI PARAMETER (Drapper & Smith)

20 C A B A yi yi x y yi yi C B *Least squares estimation gave us the line (β) that minimized C 2 A 2 B 2 C 2 SS total Total squared distance of observations from naïve mean of y Total variation SS reg Distance from regression line to naïve mean of y Variability due to x (regression) SS residual Variance around the regression line Additional variability not explained by x—what least squares method aims to minimize REGRESSION PICTURE R 2 =SSreg/SStotal


Download ppt "REGRESI LINIER SEDERHANA KULIAH #2 ANALISIS REGRESI Usman Bustaman."

Presentasi serupa


Iklan oleh Google