# Category Archives: STATISTIKA

## MEAN

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In statistics, mean has two related meanings:

• the arithmetic mean (and is distinguished from the geometric mean or harmonic mean).
• the expected value of a random variable, which is also called the population mean.

There are other statistical measures that use samples that some people confuse with averages – including ‘median’ and ‘mode’. Other simple statistical analyses use measures of spread, such as range, interquartile range, or standard deviation. For a real-valued random variable X, the mean is the expectation of X. Note that not every probability distribution has a defined mean (or variance); see the Cauchy distribution for an example.

For a data set, the mean is the sum of the values divided by the number of values. The mean of a set of numbers x1, x2, …, xn is typically denoted by , pronounced “x bar”. This mean is a type of arithmetic mean. If the data set were based on a series of observations obtained by sampling a statistical population, this mean is termed the “sample mean” () to distinguish it from the “population mean” (μ or μx). The mean is often quoted along with the standard deviation: the mean describes the central location of the data, and the standard deviation describes the spread. An alternative measure of dispersion is the mean deviation, equivalent to the average absolute deviation from the mean. It is less sensitive to outliers, but less mathematically tractable.

## MEDIAN

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In probability theory and statistics, a median is described as the numerical value separating the higher half of a sample, a population, or a probability distribution, from the lower half. The median of a finite list of numbers can be found by arranging all the observations from lowest value to highest value and picking the middle one. If there is an even number of observations, then there is no single middle value; the median is then usually defined to be the mean of the two middle values.

In a sample of data, or a finite population, there may be no member of the sample whose value is identical to the median (in the case of an even sample size), and, if there is such a member, there may be more than one so that the median may not uniquely identify a sample member. Nonetheless, the value of the median is uniquely determined with the usual definition. A related concept, in which the outcome is forced to correspond to a member of the sample, is the medoid. Read the rest of this entry

## MINITAB 16

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Setelah sebelumnya saya membuat link tentang MINITAB VERSI 15. kini saya akan berikan link buat download MINITAB 16. Minitab 16 ini sudah support dengan windows 7. Jadi sudah tidak perlu khawatir lagi buat yang PCnya pake windows 7.

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## UJI HIPOTESIS

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Hipotesis Statistik : pernyataan atau dugaan mengenai satu atau lebih populasi. Pengujian hipotesis berhubungan dengan penerimaan atau penolakan suatu hipotesis. Kebenaran (benar atau salahnya ) suatu hipotesis tidak akan pernah diketahui dengan pasti, kecuali kita memeriksa seluruh populasi. (Memeriksa seluruh populasi? Apa mungkin?). Lalu apa yang kita lakukan, jika kita tidak mungkin memeriksa seluruh populasi untuk memastikan kebenaran suatu hipotesis? Kita dapat mengambil sampel acak, dan menggunakan informasi (atau bukti) dari sampel itu untuk menerima atau menolak suatu hipotesis.

Nilai yang diasumsikan dinyatakan dalam :

•  Ho atau null hypothesis
•  H1 atau alternative hypothesis

Null hypothesis diuji berhadapan dengan alternative hypothesis. Teori pengujian hipotesis akan memutuskan apakah apakah Ho ditolak atau diterima. Keputusan menolak atau menerima didasarkan pada test statistik yang diperoleh dari sampel, setelah dibandingkan dengan nilai kritis dari distribusi statistik yang bersangkutan dalam tabel.

## DURBIN-WATSON STATISTIC

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In statistics, the Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation (a relationship between values separated from each other by a given time lag) in the residuals (prediction errors) from a regression analysis. It is named after James Durbin and Geoffrey Watson. However, the small sample distribution of this ratio was derived in a path-breaking article by John von Neumann (von Neumann, 1941). Durbin and Watson (1950, 1951) applied this statistic to the residuals from least squares regressions, and developed bounds tests for the null hypothesis that the errors are serially independent (not autocorrelated) against the alternative that they follow a first order autoregressive process. Later, John Denis Sargan and Alok Bhargava developed several von Neumann–Durbin–Watson type test statistics for the null hypothesis that the errors on a regression model follow a process with a unit root against the alternative hypothesis that the errors follow a stationary first order autoregression (Sargan and Bhargava, 1983). Read the rest of this entry

## ANALISIS REGRESI GANDA

Misalnya kita ingin meramal berapa berat badan seseorang jika kita memiliki data tentang tinggi badan dan umur. Data tersebut kita masukkan ke dalam MINITAB seperti dibawah ini.

## ANALISIS REGRESI LINEAR

Langkah – Langkah menggunakan Minitab dalam Penyelesain Statistik.

Contoh :

Diambil Sebuah data Untuk Matematika dan Fisika

 Nomor Siswa Matematika Fisika 1. 60 80 2. 45 69 3. 50 71 4. 60 85 5. 50 80 6. 65 82 7. 60 89 8. 65 93 9. 50 76 10. 65 86 11. 45 71 12. 50 69