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Summary Applied Statistics 1 Erasmus
Need a thorough summary for your IBEB Applied Statistics 1 exam on the Erasmus University Rotterdam? Don’t look any further. With this, the exam will be a piece of cake!
“An open book exam, I hope I can find everything. If it’s an open book exam, the material will probably be difficult to understand. If only I had a good overview of the material instead of that thick book”
Does this line of thought seem familiar? If so, you’re in luck. Because Reken Maar Verslagen has the solution for you.
The first applied statistics course (FEB11005X) is one of the hardest courses within the International Bachelor Economics and Business Economics (IBEB) on Erasmus Rotterdam (EUR). Maybe you’ve experienced this already for yourself. Subjects like correlation, binominal distribution, t-tests and the probability theory are examples of subjects that students usually struggle with. Do you want to get going immediately so that you can pass the exam with the best grade possible? With the summary applied statistics 1 for IBEB student from the Erasmus university in Rotterdam you will be able to! Do you want to know more about the applied statistics course or get a short introduction to certain topics within the course? Read more about what to expect below. We will give you some inside info.
Do you want to get going immediately so that you can pass the exam with the best grade possible? Click here to order your summary applied statistics today!
info. Want to know more about the applied statistics course or like to get a short introduction to the core subjects of the course? Continue reading to find out what to expect.
Summary applied statistics 1 Erasmus University Rotterdam: Core concepts
Some knowledge of statistics is needed for almost every university level study. For example for checking whether the research you’ve done actually was significant and gave new insights. Statistics can also be used to analyze data and to discover causality in specific data sets. In block 5 of this year you will need to do a research on economies of scale, for which you will need the insights given by this statistics course. You need to be able to test hypothesis and draw correct conclusions from the results.
In the summary applied statistics 1, which you can order here, various statistical methods will be introduced and explained using examples. To help you on your way, we will go through the following core concepts of applied statistics:
· Core concept 1: Correlation
· Core concept 2: Probability models
· Core concept 3: One sample t-test
· Core concept 4: Binominal distribution
Core concept 5: Power and faults in testing
Summary Applied Statistics 1 Erasmus Rotterdam – Core concept 1: Correlation
Correlation is an important concept within the world of statistics. Correlation between two variables can be calculated using a formula. The outcome of this describes the relationship between the two variables. It describes how strong and in what direction that relationship is.
Correlation always lies between -1 and 1. A positive relationship between the variables causes the correlation to be positive as well (0 < correlation < 1) and vise versa (-1 < correlation < 0). A value close to zero shows a weaker relationship then when the correlation is closer to -1 or 1. A correlation of -1 would mean that the two variables are perfectly negatively correlated, and therefore it shows a negative relationship. Note that correlation only describes linear relationships and does not account for outliers of quadratic relations.
Curious to know how various correlations are shown in a scatterplot or want to know how to exactlycalculate correlation between two variables? Order your summary applied statistics 1 here!
Summary Applied Statistics 1 Erasmus Rotterdam – Core concept 2: Probability models
A probability model describes the distribution of probabilities of a random event, which is dependent on the sample space and the event itself. These probability models are commonly used within the world of statistics.
The so called sample space are all the possible outcomes of an event. What this entails is easily shown by looking at common dices. When rolling one dice there a six possible outcomes, namely 1, 2, 3, 4, 5 or 6. All of these outcomes together are the sample space. When rolling two dices the outcomes increase by a lot to 36 possible outcomes. (If you count 2 and 3 as a different outcome then rolling 3 and 2 etc.)
An event is one or multiple outcomes from the random event. When rolling one dice this can be rolling a 3. When rolling 2 it could be rolling 1 and 3. If we’re talking about multiple outcomes we’re talking about, for example, repeated rolling of the dice(s).
Probability has a few standard rules that form the basics of calculating with probabilities. It is expected of you to know these rules and to be able to calculate with probabilities. You can find what these rules entail or how to calculate using probabilities in this summary applied statistics Erasmus Rotterdam.
Summary Applied Statistics 1 Erasmus Rotterdam – Core concept 3: One sample test
T-tests are something you will run into a lot during your studies. Therefore it is important to really understand them. When you do not know the standard deviation of a population it is still possible to make an estimation of the population average using a t-distribution. This distribution uses the standard deviation of the sample to be able to say something about the entire population. The t-statistic is only valid when the population has a normal distribution. With the one-sample t-test you try to say something about one population or group. The steps you need to take to test a certain hypothesis using a t-test are the following:
Step 1: Form hypothesis.
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 and 3 and draw conclusions from the results.
Still not certain you understand this subject? Order this summary applied statistics quickly to start practicing with the steps!
Summary Applied Statistics 1 Erasmus Rotterdam – Core concept 4: Binominal distribution
The binominal distribution is one of the three distributions that show the chance of one or multiple outcomes. The others are the uniform- and Poisson distributions. These distributions can be very useful, but can be quite hard to understand completely.
With the binominal distribution there are only two options, either a certain action fails or succeeds. With this action there is a certain chance it fails or succeeds, which is denoted by 𝑝.
A binominal distribution actually contains just three value for which you have to account for.
𝑛 The number of observations
𝑝 The chance of success
𝑘 The amount of times you want to achieve success
A common example used to explain the binominal distribution is that of flipping a coin. For instance, you can see flipping heads as a success and flipping tails as a failure. The chance for both is of course 0,5. Therefore the chance of success = 𝑝 = 0,5. Another example is guessing on a multiple choice question where you have no idea whatsoever what would be the right answer. When there are four possibilities and only one right answer, the chance you succeed and choose the right answer will be 𝑝 = 0,25. Follow along with the following practice question to see whether you understand the basics of the binominal distribution:
Suppose we ask three random people on the street, Presnel, Karim and Nabil, if they have a subscription to the newspaper. The probability of someone answering “yes” (which we consider a success) is 40%. What is the probability exactly two people answer yes?
The answer to this practice question is 0,288. Curious how we got this answer or do you want to know more about all three distributions? Order your summary applied statistics here!
Summary Applied Statistics 1 Erasmus Rotterdam – Core concept 5: Power and faults in testing
With statistical test we can almost never conclude things based on hard evidence. There will always be a chance that the results aren’t accurate. Of course, we want to avoid making wrong conclusions. Therefore we judge a test on its ability to discover whether 𝐻0 is false. To do this we use the power of a test. The power of a test is the chance that the test, at a standard level of significance (5%), rejects the null hypothesis 𝐻0 while the value of the alternative hypothesis is accurate. The power of the test basically indicates how well a certain test does its job.
There are a few steps you need to follow to calculate the power of a test:
Step 1: Form hypotheses.
Step 2: Find the critical value 𝒛 ∗ in table D.
Step 3: Calculate the corresponding value 𝒙̅.
Step 4: Calculate the z-score again using the newly found value of 𝝁.
Step 5: Transform the z-score into a P-value. This is the power.
You can find how to perform these step accurately in the summary applied statistics 1 Erasmus Rotterdam, which you can order here.
Summary applied statistics 1 Erasmus Rotterdam: Structure of the exam
The Applied Statistics 1 course is worth 4 ECTs. The final grade will depend on how you do in class and on the exam. There are no midterms and you can do the in class assignments in a group. When you’ve prepared a bit beforehand they are not too difficult to do. Try your best during these assignments, as you can earn a bonus of 0,5 on your final grade!
The final exam consists of 40 right/false questions. A correct answer is worth two points. During the exam you can also choose to put a question mark, this will be worth 1 point. To pass the exam you would need a minimum of 60 points.
Like briefly mentioned before, the exam is an open book exam. You can use your book and notes written in the book during the exam. It can be very useful to use some of the blank pages to make a little summary for yourself. This can safe some much needed time you would have lost searching through the book. You can only use a normal calculator like in other courses.
The final exam will be about chapters 1 through 8 and 10 in the book, all slides and remaining material covered during lectures.
Summary applied statistics 1 Erasmus Rotterdam, everything you need to succeed
As you have read, there are a lot of different subject introduced during applied statistics 1. It’s important to know the basics well, as you will also need them for the second applied statistics course.
The summary from Reken Maar offers you a helping hand. All subjects mentioned above and more are thoroughly explained through examples and practice questions. Order your summaries here. We at Reken Maar wish you all the best in your studies!
Interested in statistics? What kind of jobs can you get?
Occupations that use statistics on a daily basis vary a lot. Nowadays, statistics are a valuable source of information for various sectors. For example doing research on trends for the CBS, statistical research about demographic traits of risk analysis. Examples of job that commonly use statistics are:
• Analyst
• Trend watcher
• PhD
• Consultant
• Real Estate agent
• Epidemiologist
• Lab technician
• Researcher
• Statistician
We at Reken Maar have a lot of experience, having completed courses ourselves. Therefore we have put all the knowledge and information to complete this course with flying colors in our summary applied statistics 1 specifically for IBEB students at Erasmus University.
Order your own summary applied statistics 1 here!
We at Reken Maar wish you all the best with the preparations and the best of luck with your exam!
