Mengidentifikasi korelasi antar variabel

Anda telah melihat bahwa menambahkan garis tren pada diagram sebar adalah cara yang sangat baik untuk memahami kekuatan dan arah hubungan antar variabel.

Sebuah aplikasi telah disediakan untuk menampilkan diagram sebar dengan garis tren untuk data tentang kota. Setiap titik data mewakili sebuah kota, dan posisinya pada plot ditentukan oleh biaya sebotol air dan biaya keanggotaan gym bulanan di kota tersebut.

Tugas Anda adalah mendeskripsikan koefisien korelasi Pearson untuk biaya sebotol air (£) sebagai variabel dependen dan jam kerja tahunan sebagai variabel independen.

Latihan ini adalah bagian dari kursus

Pengantar Statistika

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Latihan ini adalah bagian dari kursus

Pengantar Statistika

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Mulai Kursus Gratis

Summary statistics gives you the tools you need to describe your data. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

Exercise 1: What is statistics?Exercise 2: Using statistics in the real-world Exercise 3: Identifying data types Exercise 4: Descriptive vs. Inferential statistics Exercise 5: Measures of center Exercise 6: Typical number of robberies per London Borough Exercise 7: Choosing a measure Exercise 8: London Boroughs with most frequent crimes Exercise 9: Measures of spread Exercise 10: Defining measures of spread Exercise 11: Box plots for measuring spread Exercise 12: Which crime has the larger standard deviation

Probability underpins a large part of statistics, where it is used to calculate the chance of events occurring. You'll work with real-world sales data and learn how data with different values can be interpreted as a probability distribution. You'll find out about discrete and continuous probability distributions, including the discovery of the normal distribution and how it occurs frequently in natural events!

Exercise 1: What are the chances?Exercise 2: What is more likely?Exercise 3: Chances of the next sale being more than the mean Exercise 4: Conditional probability Exercise 5: Dependent vs. Independent events Exercise 6: Orders of more than 10 basket products Exercise 7: Discrete distributions Exercise 8: Identifying distributions Exercise 9: Sample mean vs. Theoretical mean Exercise 10: Continuous distributions Exercise 11: Discrete vs. Continuous distributions Exercise 12: Finding the normal distribution Exercise 13: Probability with a uniform distribution

It's time to explore more probability distributions. You'll learn about the binomial distribution for visualizing the probability of binary outcomes, and one of the most important distributions in statistics, the normal distribution. You'll see how distributions can be described by their shape, along with discovering the Poisson distribution and its role in calculating the probabilities of events occuring over time. You'll also gain an understanding of the central limit theorem!

Exercise 1: The binomial distribution Exercise 2: Recognizing a binomial distribution Exercise 3: How probability affects the binomial distribution Exercise 4: Identifying n and p Exercise 5: The normal distribution Exercise 6: Recognizing the normal distribution Exercise 7: What makes the normal distribution special?Exercise 8: Identifying skewness Exercise 9: Describing distributions using kurtosis Exercise 10: The central limit theorem Exercise 11: Visualizing sampling distributions Exercise 12: The CLT vs. The law of large numbers Exercise 13: When to use the central limit theorem Exercise 14: The Poisson distribution Exercise 15: Identifying Poisson processes Exercise 16: Recognizing lambda in the Poisson distribution

In the final chapter, you'll be introduced to hypothesis testing and how it can be used to accurately draw conclusions about a population. You'll discover correlation and how it can be used to quantify a linear relationship between two variables. You'll find out about experimental design techniques such as randomization and blinding. You'll also learn about concepts used to minimize the risk of drawing the wrong conclusion about the results of hypothesis tests!

Exercise 1: Uji hipotesis Exercise 2: Sinar matahari dan tidur Exercise 3: Alur kerja pengujian hipotesis Exercise 4: Variabel independen dan dependen Exercise 5: Eksperimen Exercise 6: Mengidentifikasi uji terkontrol Exercise 7: Mengapa menggunakan pengacakan?Exercise 8: Korelasi Exercise 9: Mengidentifikasi korelasi antar variabel

Latihan Saat Ini

Exercise 10: Apa yang dapat diberitahukan oleh korelasi kepada Anda?Exercise 11: Variabel pengacau Exercise 12: Menafsirkan hasil uji hipotesis Exercise 13: Tingkat signifikansi vs. p-value Exercise 14: Kesalahan Tipe I dan Tipe II Exercise 15: Selamat!