0.1 * 2 = 0.2 0.2 * 2 = 0.4 0.4 * 2 = 0.8 0.8 * 2 = 1.6 0.6 * 2 = 1.2 0.2 * 2 = … (repeats)
What is your specific question? If it is about how to add them, then check out comments 3 and 4. If it is about how to place the point, check out comments 30 and 31. (If it is about something else, or you need more clarification, please let me know.)
I am new to computer arithmetic so sorry if it seemed silly to ask.
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Every developer has heard of Fibonacci and his sequence. The Fibonacci search algorithm is practically a variation of the binary search algorithm. In fact the only difference is that the binary search algorithm divides the list into two equal parts, while the Fibonacci search divides it in two but not equal parts. In fact sometimes it is faster to search if you divide the list by such non equal sub-lists. However the length of the sub-lists is not random.
The complexity of the binary search algorithm is intuitively clear – O(log(n)), which makes it far more effective than the sequential search.
It is clear that the ratio of any two consecutive numbers in the Fibonacci sequence is practically forming the golden ratio. This can lead us to another variation of Fibonacci and binary search – the golden section search. The only different thing is that you’ve to divide the length of the list in two parts exactly by the golden ratio.
It is useless to mention examples of its use. This algorithm is easy to implement and in the same times it is very fast. Yes, indeed, this algorithm is only possible on sorted lists and this is a limitation. Also, as I said, compared to the jump search here we have more than one jump back in most of the cases, which sometimes can be more expensive than jump forward. However is this the fastest search algorithm? I’ll try to answer this question in my next article.
The binary search is perhaps the most famous and best suitable search algorithm for sorted arrays. Indeed when the array is sorted it is useless to check every single item against the desired value. Of course a better approach is to jump straight to the middle item of the array and if the item’s value is greater than the desired one, we can jump back again to the middle of the interval. Thus the new interval is half the size of the initial one.
$list = array(0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144); $x = 55; function iterative_binary_search($x, $list) $left = 0; $right = count($list)-1; while ($left > 1; if ($list $mid == $x) return $mid; elseif ($list $mid > $x) $right = $mid - 1; elseif ($list $mid < $x) $left = $mid + 1; return -1; echo iterative_binary_search($x, $list);
If the searched value is greater than the one placed at the middle of the sorted array, we can jump forward. Again on each step the considered list is getting half as long as the list on the previous step, as shown on the image bellow.
The complexity both of the Fibonacci and the golden section search algorithm is identical with the complexity of the binary search. However these two algorithms are rarely used in practice. Also it is more difficult to implement these two algorithms than the binary search and their advantage depends on specifically dispersed data.
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Statistics Solutions provides a data analysis plan template for the binary logistic regression analysis. You can use this template to develop the data analysis section of your dissertation or research proposal.
Copy and paste the following into a word document to use as your data analysis plan template.
The template includes research questions stated in statistical language, analysis justification and assumptions of the analysis. Simply edit the blue text to reflect your research information and you will have the data analysis plan for your dissertation or research proposal.
Binary logistic regression analysis, by design, overcomes many of the restrictive assumptions of linear regressions. For example, linearity, normality and equal variances are not assumed, nor is it assumed that the error term variance is normally distributed. The major assumption is that the outcome variable must be dichotomous. There should be no multicollinearity among the independent variables, there should be no outliers, and there should be a linear relationship between the odds ratio and the independent variable. Linearity with an ordinal or interval independent variable and the odds ratio can be checked by creating a new variable that divides the existing independent variable into categories of equal intervals and conducting the same regression on these newly categorized versions as categorical variables. Linearity is shown if the B coefficients increase or decrease in linear steps. Finally, a larger sample is recommended with the maximum likelihood method; using discrete variables requires that there are enough responses in each category.
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thank you, Carolyn!! you are the BEST!!!
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I have a 6year old who knows his abc. How CN I make it fun for him to where he understands he’s good with everything else it’s jus his abc. Can u help me find away to help.
10. Move and Groove the ABC’s — Get up and move to the ABC’s with ABC Exercise Cards or get leafy with an Outdoor Alphabet Hunt.
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Large-cap, blue-chip companies are not exactly the hottest commodities on the current market. But one of them has been rewarding income investors for decades and will likely continue to do so. I’m talking about Procter & Gamble Co (NYSE:PG), a multinational consumer goods company headquartered in Cincinnati, Ohio.
Question from a reader: What is your opinion on a short strangle vs a short straddle? I understand the same unlimited risk will be there because you are trading naked options. I found that one strategy I have had some success with in paper trading is using short strangles around earnings to take advantage of large drops in volatility.