Distribution of the raw materials consumed by paper production in Energy inputs for paper production from to in kilowatt hours per ton of paper.
Imports and exports of paper and cardboard worldwide to in 1, metric tons. Import and export of paper and cardboard, an international comparison in in 1, metric tons. Forest, paper and packaging companies with the highest earnings worldwide in in million U. Revenue of Kimberly-Clark from to in million U. Financial key figures of the U. Gross output of United States paper mills from to in million U.
Value added by the United States paper products manufacturing from to in million U. Total printing and writing paper supply in the U. Production capacity of the leading United States paper packaging companies in in 1, tons.
Global consumption of paper and cardboard to in million metric tons. Consumption of paper and paperboard in selected countries worldwide in in 1, metric tons. Paper consumption per capita worldwide in , by region in kilograms. Per capita consumption of paper and board in the U. Total paper and paperboard recovery in the United States from to in million tons. Paper and paperboard recovery rate in the United States from to Utilization of recovered paper and paperboard in the U.
Distribution of the utilization of recovered paper and paperboard in the United States in , by type. Paper and cardboard - imports and exports in selected countries Production of paper and cardboard worldwide Consumption of paper and cardboard Statista offers dossiers and reports on over industries.
With Statista you are always able to make informed decisions and boost your work efficiency. Feel free to contact us anytime using our contact form or visit our FAQ page. Statista now offers industry insights from a global perspective for 34 industries. The reports provide crucial information on the trends moving all major industries. Is Italy speeding up on electric mobility or is the next charging point still too far away? Have a deeper look into the topic with this new dossier.
Statista provides an overview on the market situation and customer attitudes around e-mobility in this country so passionate about cars. With an October deadline looming and an unpopular proposal in hand, time is running out for Theresa May to secure a deal with the European Union.
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Please see our privacy statement for details about how we use data. Industry Overview Most-viewed Statistics. Recent Statistics Popular Statistics. Smartphone market share worldwide by vendor Number of apps available in leading app stores Big Mac index - global prices for a Big Mac Revenue of the cosmetic industry in the U. Value of the leading 10 textile exporters worldwide.
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Total number of Nike retail stores worldwide Revenue and financial key figures of Coca-Cola This section of the statistics tutorial is about understanding how data is acquired and used.
The results of a science investigation often contain much more data or information than the researcher needs. This data-material, or information, is called raw data. To be able to analyze the data sensibly, the raw data is processed into " output data ". There are many methods to process the data, but basically the scientist organizes and summarizes the raw data into a more sensible chunk of data.
Any type of organized information may be called a " data set ". Then, researchers may apply different statistical methods to analyze and understand the data better and more accurately. Depending on the research, the scientist may also want to use statistics descriptively or for exploratory research. What is great about raw data is that you can go back and check things if you suspect something different is going on than you originally thought. This happens after you have analyzed the meaning of the results.
The raw data can give you ideas for new hypotheses, since you get a better view of what is going on. You can also control the variables which might influence the conclusion e.
In statistics, a parameter is any numerical quantity that characterizes a given population or some aspect of it. This part of the statistics tutorial will help you understand distribution, central tendency and how it relates to data sets. Much data from the real world is normal distributed , that is, a frequency curve, or a frequency distribution , which has the most frequent number near the middle.
Many experiments rely on assumptions of a normal distribution. This is a reason why researchers very often measure the central tendency in statistical research, such as the mean arithmetic mean or geometric mean , median or mode. The central tendency may give a fairly good idea about the nature of the data mean, median and mode shows the "middle value" , especially when combined with measurements on how the data is distributed.
Scientists normally calculate the standard deviation to measure how the data is distributed. But there are various methods to measure how data is distributed: However, the sampling distribution will not be normally distributed if the distribution is skewed naturally or has outliers often rare outcomes or measurement errors messing up the data.
One example of a distribution which is not normally distributed is the F-distribution , which is skewed to the right. So, often researchers double check that their results are normally distributed using range, median and mode. How do we know whether a hypothesis is correct or not? Why use statistics to determine this?
Using statistics in research involves a lot more than make use of statistical formulas or getting to know statistical software. Making use of statistics in research basically involves. Statistics in research is not just about formulas and calculation. Many wrong conclusions have been conducted from not understanding basic statistical concepts.
Statistics inference helps us to draw conclusions from samples of a population. When conducting experiments , a critical part is to test hypotheses against each other. Thus, it is an important part of the statistics tutorial for the scientific method. Hypothesis testing is conducted by formulating an alternative hypothesis which is tested against the null hypothesis , the common view.
The hypotheses are tested statistically against each other. The researcher can work out a confidence interval , which defines the limits when you will regard a result as supporting the null hypothesis and when the alternative research hypothesis is supported.
This means that not all differences between the experimental group and the control group can be accepted as supporting the alternative hypothesis - the result need to differ significantly statistically for the researcher to accept the alternative hypothesis. This is done using a significance test another article. Caution though, data dredging , data snooping or fishing for data without later testing your hypothesis in a controlled experiment may lead you to conclude on cause and effect even though there is no relationship to the truth.
Depending on the hypothesis, you will have to choose between one-tailed and two tailed tests. Sometimes the control group is replaced with experimental probability - often if the research treats a phenomenon which is ethically problematic , economically too costly or overly time-consuming, then the true experimental design is replaced by a quasi-experimental approach.
Often there is a publication bias when the researcher finds the alternative hypothesis correct, rather than having a "null result", concluding that the null hypothesis provides the best explanation. If applied correctly, statistics can be used to understand cause and effect between research variables.
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The statistics tutorial for the scientific method is a guide to help you understand key concepts in statistics and how they relates to the scientific method. Statistics paper help for students to help in essay. Horizontal (inter)subjective representation in the specific relation that they can make the meaning of to name but a .