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Summary Applied Statistics 1 Erasmus
Summary Applied Statistics 1 for the study Economics and Business Economics at the Erasmus University Rotterdam EUR needed? This will make the exam a easy!
"An open book test, I hope I can find everything. There's a lot to memorise and if it's an open book test, the material is bound to be tricky. If only I had a good overview of the material instead of that thick book."
Do you recognise yourself in the sentence above? Then we at Reken Maar Verslagen have the solution for you.
The subject Applied Statistics 1 (FEB11005) is one of the most difficult subjects in the first year of the Bachelor of Economics and Business Economics at Erasmus University Rotterdam EUR, perhaps you have already experienced this yourself. Topics such as correlation, binominal distribution, t-tests and probability theory are generally perceived as very difficult by students. Do you want to start working on these right away so that you finish your Applied Statistics 1 exam at Erasmus University with a good grade? With a summary for the subject Applied Statistics 1 at Erasmus University Rotterdam (EUR), you will simply pass! Read on below to find out what to expect: we'll give you some inside info.
Would you like to get started with this right now in order to pass your Applied Statistics 1 exam at Erasmus University with a good grade? Then click here To order your summary Applied Statistics 1!
Want to know even more about Applied Statistics 1 or get a brief introduction to the topics within the course? Then read on below to find out what's in store for you.
Summary Applied Statistics 1 Erasmus University Rotterdam: basic concepts
Statistics is a subject you will come across in almost every university study. After all, statistics is needed to check whether the research you have done has actually provided new insights. Statistics is also a means of analysing data and then discovering connections in the data you have bound. You will conduct a study on economies of scale in Block 5 in which you have to build your own database. Once you have created this database, you will need to test whether the hypothesis set is correct and start drawing conclusions. You can use various statistical methods for this, which you will be taught in this course.
In the summary applied statistics 1, which you here can order, these different statistical methods will be explained using examples. To get you started, we are going to go over the following basic concepts of applied statistics with you:
- Key concept 1: Correlation
- Key concept 2: Probability models
- Key concept 3: One sample t-test
- Key concept 4: Binominal distribution
- Key concept 5: Power and errors in tests
Summary Applied Statistics 1 Erasmus Rotterdam - Core concept 1: Correlation
Correlation is an important concept in statistics. Correlation can be calculated between two variables. The result describes how strong and in what direction the relationship between the two variables runs.
The correlation is always between -1 and 1. A correlation is positive when there is a positive relationship between the variables (0 < 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑒 < 1) and negative if the relationship is negative (-1 < 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑒 < 0). A correlation close to zero reflects a weaker relationship than a correlation close to -1 or 1. A correlation of -1 would mean that the two variables are perfectly negatively correlated. So here there is a strong negative relationship. Note that a correlation captures only linear relationships and is not resistant to outliers or quadratic relationships.
Curious about some examples of how different correlations are depicted in a scatterplot or knowing how to calculate the correlation between two variables? Then order the summary Applied Statistics 1 Erasmus Rotterdam here!
Summary Applied Statistics 1 Erasmus Rotterdam - Core concept 2: Probability models
A probability model describes the probability distribution of a random event. It is based on the sample space and the event itself. These models are widely used within statistics.
Sample space is all the possible outcomes of an event. This is easily represented by looking at dice. Suppose you roll one die, then there are six different outcomes, namely 1, 2, 3, 4, 5 or 6 pips. As soon as you start rolling two dice, at once there are 36 different possibilities of outcomes (throwing 2 with 3 is a different outcome here than throwing 3 with 2).
An event is an outcome or outcomes of the random event. For example, when rolling one dice, this could be the throwing of 3 pips. When throwing two dice, this could be the throwing of 1 and 3. When we speak of outcomes (plural) you are talking about repeated throws, for example.
Probability has a number of standard rules that form the basis of calculating probabilities. On the exam, you will be expected to master these. What these rules are and how to calculate with them can be found in this summary of applied statistics Erasmus Rotterdam.
Summary Applied Statistics 1 Erasmus Rotterdam - Key concept 3: One-Sample t-test
T-testing is a subject you are going to come across a lot in your studies. This is why it is important to get it right. If you don't know the standard deviation of a population, it is still possible to estimate the mean of a population using a t-distribution. This distribution uses the standard deviation of the sample, 𝑠, to make a statement about the population. This t-statistic is only valid when the population is normally distributed. With the one-sample t-test, you make a statement about one population or group. The steps taken to test a particular hypothesis using the t-test are as follows:
Step 1: Establish the hypotheses.
Step 2: Calculate the outcome of the test, the test statistic.
Step 3: Find the corresponding t-value. (Table D)
Step 4: Compare step 2 with step 3 and draw your conclusion
Quick order this summary applied statistics to start practising these steps!
Summary Applied Statistics 1 Erasmus Rotterdam - Core concept 4: Binominal distribution
The binomial distribution is one of three distributions that can give the probability of an outcome or multiple outcomes. The others are the uniform and poisson distributions. These distributions can be very useful, but are also complicated to understand.
With the binomial distribution, you only get two options you can face: a given action is a success or a failure. Whether the action is a success (or a failure) has a certain probability. We denote this probability by 𝑝.
A binomial distribution actually consists of only three values with which to calculate:
𝑛 The number of observations
𝑝 The likelihood of success
𝑘 The number of times you want to achieve success
The most commonly used example of a binomial distribution the toss of a coin. You can then say that tossing "head" is a success, and tossing "coin" is a failure. Obviously, the probability on both sides is 0.5. Therefore, the probability of success is 𝑝 = 0,5. Another example is correctly guessing a multiple-choice question where you have no idea what the answer might be. When there are four answer possibilities of which only one is right, your probability of success is 𝑝 = 0,25. Take part in the following sample task to see if you can grasp the basics of the binomial distribution:
Suppose we ask three people on the street, Presnel, Karim and Nabil, whether they subscribe to the newspaper at home. The probability that someone answers "yes" (which we consider a success) is 40%. What is the probability that exactly two people answer yes?
The answer to this example task is 0.288. Wondering how we arrived at this or want to learn more about the three distributions? Then order the summary of applied statistics via this link!
Summary Applied Statistics 1 Erasmus Rotterdam - Core concept 5: Power and errors in tests
With statistical tests, we can never actually speak of hard evidence. In testing, it always remains a probability that makes a situation plausible. Of course, we want the test we run to do its job well. We judge a test on its ability to discover whether 𝐻0 is incorrect. We can do this using the power of a test. The power is the probability that, at a standard significance level of, say, 5%, the test will reject the null hypothesis 𝐻0 rejects, when the value of the alternative hypothesis is actually true. In short, the power of a test indicates how well a particular test does its job.
Calculating power can be done via the following step-by-step plan:
Step 1: Establish the hypotheses.
Step 2: Find the critical value 𝒛 ∗ on in Table D.
Step 3: Calculate the value 𝒙̅, which belongs to the critical value found.
Step 4: Now recalculate the z-score with the new value 𝝁.
Step 5: Convert this z-score to a P-value. This is your power.
How exactly you perform these steps can be found in the summary applied statistics 1 Erasmus Rotterdam that you here can order.
Summary Applied Statistics 1 Erasmus Rotterdam: Exam structure
The Applied Statistics 1 course is a 4 point course and the grade will consist of assignments during practicals and the final exam. There are no midterms. You can do the assignments during the practicals in a group and if you have prepared them a little bit, they are not too difficult. Do your best during these practicals, as you can then score a bonus of 0.5 on your final grade!
The final exam consists of 40 correct/incorrect questions. A correct answer earns two points. It is also possible to enter a question mark, which earns you one point. To get a pass, you need a minimum of 60 points.
It is an open book exam, so you may use your book during the exam. You can also use some space in your book to write a small summary in it (in pencil). This can save a lot of time when looking it up. A graphical calculator is not allowed. You will therefore have to use a normal calculator, as you are used to in other subjects in the study of Economics.
The exam will cover chapters 1 - 8 and chapter 10 of the book, all slides and other material covered during the lectures.
Summary Applied Statistics 1 Erasmus Rotterdam, everything you need to succeed
As you have read, there are many different topics introduced in applied statistics 1. It is important to master the basic material well, as you will also need it for applied statistics 2.
The summary of Reken Maar offers you a helping hand in this. The above topics and all other subjects in the subject are discussed and explained in more detail using examples and practice exercises.
Do you find Applied Statistics interesting? What can you become with it?
Professions that use statistics are very diverse. Today, statistics is an important tool for scientific research, companies or the government, for example. Think of researching trends for CBS, statistical research on demographic characteristics or risk analysis. Examples of jobs that have a lot to do with statistics are:
- Analyst
- Trendwatcher
- PhD
- Consultant
- Estate agent
- Epidemiologist
- Laboratory technician
- Researcher
- Staticus
Because we speak from experience and know what it is like to complete this course successfully, we have collected all the necessary knowledge and information to successfully complete this course in a Summary of Applied Statistics 1 that has been compiled specifically and only for students of the Economics and Business Economics programme at Erasmus University Rotterdam.
